diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..ad4a1f17f296cc391d0e77a9efce35968742bfac --- /dev/null +++ b/.gitignore @@ -0,0 +1,176 @@ +# Created by https://www.toptal.com/developers/gitignore/api/python +# Edit at https://www.toptal.com/developers/gitignore?templates=python + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. 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0000000000000000000000000000000000000000..612ee9db5fe7be14d22f6b9300e795feac4a78a0 --- /dev/null +++ b/Datasets/TotalSegmentator.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1b823e73589521e3a31189bf18726695b04363267c945e5ed3a5723cbcb38010 +size 830561524 diff --git a/Datasets/autoPET-III.zip b/Datasets/autoPET-III.zip new file mode 100644 index 0000000000000000000000000000000000000000..8ba0a9d4be19943586ebd40cfafc6f04141c89e4 --- /dev/null +++ b/Datasets/autoPET-III.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67c63f01aa85155c8535fde62a6895687465faa40420b7e19d0913fdc088ad08 +size 180238799 diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..babeda99d00c01f03cb842b70af410dacdb62740 --- /dev/null +++ b/LICENSE @@ -0,0 +1 @@ +Data and code are released under CC-BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/). diff --git a/MedVision.py b/MedVision.py new file mode 100644 index 0000000000000000000000000000000000000000..ce9e7d496e798a3ae8f37c255c06b9c3aad4cf0d --- /dev/null +++ b/MedVision.py @@ -0,0 +1,9115 @@ +import os +import subprocess +import gzip +import json +import zipfile +import importlib +import datasets +from datasets import ( + BuilderConfig, + GeneratorBasedBuilder, + SplitGenerator, + Split, + DatasetInfo, + Features, + Value, + Sequence, +) +from huggingface_hub import snapshot_download + + +if os.environ.get("MedVision_DATA_DIR") is None: + raise ValueError( + "Environment variable MedVision_DATA_DIR must be set to specify download directory" + ) + +logger = datasets.logging.get_logger("MedVision") + +# Set parameters +RAMDOM_SEED = 1024 +SPLIT_TRAIN_RATIO = 0.7 + +_CITATION = """\ +Please cite the paper if you use the MedVision dataset: +- (to be updated) +""" + +_DESCRIPTION = """\ +This is the official release of the MedVision dataset. +""" + +_HOME_PAGE = "https://huggingface.co/datasets/YongchengYAO/MedVision" + +_LICENSE = "CC BY-NC 4.0" + + +class MedVisionConfig(BuilderConfig): + """BuilderConfig for MedVision.""" + + def __init__( + self, + features_dict, + dataset_name, + taskType, + taskID, + imageType, + imageSliceType=None, + split=None, + num_proc=1, + **kwargs, + ): + + # Validate taskType + valid_task_types = [ + "Mask-Size", + "Box-Size", + "Tumor-Lesion-Size", + "Biometrics-From-Landmarks", + "Biometrics-From-Landmarks-Distance", + "Biometrics-From-Landmarks-Angle", + ] + if taskType.lower() not in [t.lower() for t in valid_task_types]: + raise ValueError( + f"\nError: taskType must be one of {valid_task_types}, got {taskType}\n" + ) + + # TaskID starts from 01 + if not taskID.isdigit() or len(taskID) != 2 or int(taskID) < 1: + raise ValueError( + f"\nError: taskID must be a 2-digit string starting from '01', got {taskID}\n" + ) + + # Validate imageType + valid_image_types = ["2D", "3D"] + if imageType.lower() not in [t.lower() for t in valid_image_types]: + raise ValueError( + f"\nError: imageType must be one of {valid_image_types}, got {imageType}\n" + ) + + # Validate imageSliceType + if imageType.lower() == "2d": + valid_slice_types = ["sagittal", "coronal", "axial"] + if not imageSliceType or imageSliceType.lower() not in valid_slice_types: + raise ValueError( + f"\nError: For 2D images, imageSliceType must be one of {valid_slice_types}, got {imageSliceType}\n" + ) + if imageType.lower() == "3d" and imageSliceType is not None: + raise ValueError( + f"\nError: For 3D images, imageSliceType must be None or removed, got {imageSliceType}\n" + ) + + # Validate split + if split is not None: + valid_splits = ["train", "test"] + if split.lower() not in valid_splits: + raise ValueError( + f"\nError: split must be one of {valid_splits}, got {split}\n" + ) + + # Check: 3D images not supported for Mask-Size task + if taskType.lower() == "Mask-Size" and imageType.lower() == "3D": + raise ValueError( + f"\nError: 3D images are not supported for Mask-Size task, got {imageType}\n" + ) + + # Set number of workers for multiprocessing + self.num_proc = num_proc + + super().__init__( + version="1.0.0", **kwargs + ) # dataset version; + self.features_dict = features_dict + self.dataset_name = dataset_name + self.taskType = taskType + self.taskID = taskID + self.imageType = imageType + self.imageSliceType = imageSliceType + self.split = split + + +class MedVision(GeneratorBasedBuilder): + """ + MedVision dataset. + + NOTE: To update the features turned by the load_dataset() method, the followings should be updated: + - the feature dict in this class + - the dict yielded by the _generate_examples() method + """ + + # The feature dict for the task: + # - Mask-Size + features_dict_MaskSize = { + "taskID": Value("string"), + "taskType": Value("string"), + "image_file": Value("string"), + "mask_file": Value("string"), + "slice_dim": Value("uint8"), + "slice_idx": Value("uint16"), + "label": Value("uint16"), + "image_size_2d": Sequence(Value("uint16"), length=2), + "pixel_size": Sequence(Value("float16"), length=2), + "image_size_3d": Sequence(Value("uint16"), length=3), + "voxel_size": Sequence(Value("float16"), length=3), + "pixel_count": Value("uint32"), + "ROI_area": Value("float16"), + } + + # The feature dict for the task: + # - Box-Size + features_dict_BoxSize = { + "taskID": Value("string"), + "taskType": Value("string"), + "image_file": Value("string"), + "mask_file": Value("string"), + "slice_dim": Value("uint8"), + "slice_idx": Value("uint16"), + "label": Value("uint16"), + "image_size_2d": Sequence(Value("uint16"), length=2), + "pixel_size": Sequence(Value("float16"), length=2), + "image_size_3d": Sequence(Value("uint16"), length=3), + "voxel_size": Sequence(Value("float16"), length=3), + "bounding_boxes": Sequence( + { + "min_coords": Sequence(Value("uint16"), length=2), + "max_coords": Sequence(Value("uint16"), length=2), + "center_coords": Sequence(Value("uint16"), length=2), + "dimensions": Sequence(Value("uint16"), length=2), + "sizes": Sequence(Value("float16"), length=2), + }, + ), + } + + features_dict_BiometricsFromLandmarks = { + "taskID": Value("string"), + "taskType": Value("string"), + "image_file": Value("string"), + "landmark_file": Value("string"), + "slice_dim": Value("uint8"), + "slice_idx": Value("uint16"), + "image_size_2d": Sequence(Value("uint16"), length=2), + "pixel_size": Sequence(Value("float16"), length=2), + "image_size_3d": Sequence(Value("uint16"), length=3), + "voxel_size": Sequence(Value("float16"), length=3), + "biometric_profile": { + "metric_type": Value("string"), + "metric_map_name": Value("string"), + "metric_key": Value("string"), + "metric_value": Value("float16"), + "metric_unit": Value("string"), + "slice_dim": Value("uint8"), + }, + } + + features_dict_TumorLesionSize = { + "taskID": Value("string"), + "taskType": Value("string"), + "image_file": Value("string"), + "landmark_file": Value("string"), + "mask_file": Value("string"), + "slice_dim": Value("uint8"), + "slice_idx": Value("uint16"), + "label": Value("uint16"), + "image_size_2d": Sequence(Value("uint16"), length=2), + "pixel_size": Sequence(Value("float16"), length=2), + "image_size_3d": Sequence(Value("uint16"), length=3), + "voxel_size": Sequence(Value("float16"), length=3), + "biometric_profile": Sequence( + { + "metric_type": Value("string"), + "metric_map_name": Value("string"), + "metric_key_major_axis": Value("string"), + "metric_value_major_axis": Value("float16"), + "metric_key_minor_axis": Value("string"), + "metric_value_minor_axis": Value("float16"), + "metric_unit": Value("string"), + }, + ), + } + + BUILDER_CONFIGS = [ + # AbdomenAtlas1.0Mini:Mask-Size:Task01 + MedVisionConfig( + name="AbdomenAtlas1.0Mini_MaskSize_Task01_Sagittal_Train", + dataset_name="AbdomenAtlas1.0Mini", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="AbdomenAtlas1.0Mini_MaskSize_Task01_Sagittal_Test", + dataset_name="AbdomenAtlas1.0Mini", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="AbdomenAtlas1.0Mini_MaskSize_Task01_Coronal_Train", + dataset_name="AbdomenAtlas1.0Mini", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="AbdomenAtlas1.0Mini_MaskSize_Task01_Coronal_Test", + dataset_name="AbdomenAtlas1.0Mini", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="AbdomenAtlas1.0Mini_MaskSize_Task01_Axial_Train", + dataset_name="AbdomenAtlas1.0Mini", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="AbdomenAtlas1.0Mini_MaskSize_Task01_Axial_Test", + dataset_name="AbdomenAtlas1.0Mini", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # AbdomenAtlas1.0Mini:Box-Size:Task01 + MedVisionConfig( + name="AbdomenAtlas1.0Mini_BoxSize_Task01_Sagittal_Train", + dataset_name="AbdomenAtlas1.0Mini", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="AbdomenAtlas1.0Mini_BoxSize_Task01_Sagittal_Test", + dataset_name="AbdomenAtlas1.0Mini", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="AbdomenAtlas1.0Mini_BoxSize_Task01_Coronal_Train", + dataset_name="AbdomenAtlas1.0Mini", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="AbdomenAtlas1.0Mini_BoxSize_Task01_Coronal_Test", + dataset_name="AbdomenAtlas1.0Mini", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="AbdomenAtlas1.0Mini_BoxSize_Task01_Axial_Train", + dataset_name="AbdomenAtlas1.0Mini", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="AbdomenAtlas1.0Mini_BoxSize_Task01_Axial_Test", + dataset_name="AbdomenAtlas1.0Mini", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # AbdomenCT-1K:Mask-Size:Task01 + MedVisionConfig( + name="AbdomenCT-1K_MaskSize_Task01_Sagittal_Train", + dataset_name="AbdomenCT-1K", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="AbdomenCT-1K_MaskSize_Task01_Sagittal_Test", + dataset_name="AbdomenCT-1K", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="AbdomenCT-1K_MaskSize_Task01_Coronal_Train", + dataset_name="AbdomenCT-1K", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="AbdomenCT-1K_MaskSize_Task01_Coronal_Test", + dataset_name="AbdomenCT-1K", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="AbdomenCT-1K_MaskSize_Task01_Axial_Train", + dataset_name="AbdomenCT-1K", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="AbdomenCT-1K_MaskSize_Task01_Axial_Test", + dataset_name="AbdomenCT-1K", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # AbdomenCT-1K:Box-Size:Task01 + MedVisionConfig( + name="AbdomenCT-1K_BoxSize_Task01_Sagittal_Train", + dataset_name="AbdomenCT-1K", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="AbdomenCT-1K_BoxSize_Task01_Sagittal_Test", + dataset_name="AbdomenCT-1K", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="AbdomenCT-1K_BoxSize_Task01_Coronal_Train", + dataset_name="AbdomenCT-1K", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="AbdomenCT-1K_BoxSize_Task01_Coronal_Test", + dataset_name="AbdomenCT-1K", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="AbdomenCT-1K_BoxSize_Task01_Axial_Train", + dataset_name="AbdomenCT-1K", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="AbdomenCT-1K_BoxSize_Task01_Axial_Test", + dataset_name="AbdomenCT-1K", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # ACDC:Mask-Size:Task01 + MedVisionConfig( + name="ACDC_MaskSize_Task01_Sagittal_Train", + dataset_name="ACDC", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="ACDC_MaskSize_Task01_Sagittal_Test", + dataset_name="ACDC", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="ACDC_MaskSize_Task01_Coronal_Train", + dataset_name="ACDC", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="ACDC_MaskSize_Task01_Coronal_Test", + dataset_name="ACDC", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="ACDC_MaskSize_Task01_Axial_Train", + dataset_name="ACDC", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="ACDC_MaskSize_Task01_Axial_Test", + dataset_name="ACDC", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # ACDC:Box-Size:Task01 + MedVisionConfig( + name="ACDC_BoxSize_Task01_Sagittal_Train", + dataset_name="ACDC", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="ACDC_BoxSize_Task01_Sagittal_Test", + dataset_name="ACDC", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="ACDC_BoxSize_Task01_Coronal_Train", + dataset_name="ACDC", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="ACDC_BoxSize_Task01_Coronal_Test", + dataset_name="ACDC", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="ACDC_BoxSize_Task01_Axial_Train", + dataset_name="ACDC", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="ACDC_BoxSize_Task01_Axial_Test", + dataset_name="ACDC", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # AMOS22:Mask-Size:Task01 + MedVisionConfig( + name="AMOS22_MaskSize_Task01_Sagittal_Train", + dataset_name="AMOS22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="AMOS22_MaskSize_Task01_Sagittal_Test", + dataset_name="AMOS22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="AMOS22_MaskSize_Task01_Coronal_Train", + dataset_name="AMOS22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="AMOS22_MaskSize_Task01_Coronal_Test", + dataset_name="AMOS22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="AMOS22_MaskSize_Task01_Axial_Train", + dataset_name="AMOS22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="AMOS22_MaskSize_Task01_Axial_Test", + dataset_name="AMOS22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # AMOS22:Mask-Size:Task02 + MedVisionConfig( + name="AMOS22_MaskSize_Task02_Sagittal_Train", + dataset_name="AMOS22", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="AMOS22_MaskSize_Task02_Sagittal_Test", + dataset_name="AMOS22", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="AMOS22_MaskSize_Task02_Coronal_Train", + dataset_name="AMOS22", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="AMOS22_MaskSize_Task02_Coronal_Test", + dataset_name="AMOS22", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="AMOS22_MaskSize_Task02_Axial_Train", + dataset_name="AMOS22", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="AMOS22_MaskSize_Task02_Axial_Test", + dataset_name="AMOS22", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # AMOS22:Box-Size:Task01 + MedVisionConfig( + name="AMOS22_BoxSize_Task01_Sagittal_Train", + dataset_name="AMOS22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="AMOS22_BoxSize_Task01_Sagittal_Test", + dataset_name="AMOS22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="AMOS22_BoxSize_Task01_Coronal_Train", + dataset_name="AMOS22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="AMOS22_BoxSize_Task01_Coronal_Test", + dataset_name="AMOS22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="AMOS22_BoxSize_Task01_Axial_Train", + dataset_name="AMOS22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="AMOS22_BoxSize_Task01_Axial_Test", + dataset_name="AMOS22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # AMOS22:Box-Size:Task02 + MedVisionConfig( + name="AMOS22_BoxSize_Task02_Sagittal_Train", + dataset_name="AMOS22", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="AMOS22_BoxSize_Task02_Sagittal_Test", + dataset_name="AMOS22", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="AMOS22_BoxSize_Task02_Coronal_Train", + dataset_name="AMOS22", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="AMOS22_BoxSize_Task02_Coronal_Test", + dataset_name="AMOS22", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="AMOS22_BoxSize_Task02_Axial_Train", + dataset_name="AMOS22", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="AMOS22_BoxSize_Task02_Axial_Test", + dataset_name="AMOS22", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # autoPET-III:Mask-Size:Task01 + MedVisionConfig( + name="autoPET-III_MaskSize_Task01_Sagittal_Train", + dataset_name="autoPET-III", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="autoPET-III_MaskSize_Task01_Sagittal_Test", + dataset_name="autoPET-III", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="autoPET-III_MaskSize_Task01_Coronal_Train", + dataset_name="autoPET-III", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="autoPET-III_MaskSize_Task01_Coronal_Test", + dataset_name="autoPET-III", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="autoPET-III_MaskSize_Task01_Axial_Train", + dataset_name="autoPET-III", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="autoPET-III_MaskSize_Task01_Axial_Test", + dataset_name="autoPET-III", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # autoPET-III:Mask-Size:Task02 + MedVisionConfig( + name="autoPET-III_MaskSize_Task02_Sagittal_Train", + dataset_name="autoPET-III", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="autoPET-III_MaskSize_Task02_Sagittal_Test", + dataset_name="autoPET-III", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="autoPET-III_MaskSize_Task02_Coronal_Train", + dataset_name="autoPET-III", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="autoPET-III_MaskSize_Task02_Coronal_Test", + dataset_name="autoPET-III", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="autoPET-III_MaskSize_Task02_Axial_Train", + dataset_name="autoPET-III", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="autoPET-III_MaskSize_Task02_Axial_Test", + dataset_name="autoPET-III", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # autoPET-III:Box-Size:Task01 + MedVisionConfig( + name="autoPET-III_BoxSize_Task01_Sagittal_Train", + dataset_name="autoPET-III", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="autoPET-III_BoxSize_Task01_Sagittal_Test", + dataset_name="autoPET-III", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="autoPET-III_BoxSize_Task01_Coronal_Train", + dataset_name="autoPET-III", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="autoPET-III_BoxSize_Task01_Coronal_Test", + dataset_name="autoPET-III", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="autoPET-III_BoxSize_Task01_Axial_Train", + dataset_name="autoPET-III", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="autoPET-III_BoxSize_Task01_Axial_Test", + dataset_name="autoPET-III", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # autoPET-III:Box-Size:Task02 + MedVisionConfig( + name="autoPET-III_BoxSize_Task02_Sagittal_Train", + dataset_name="autoPET-III", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="autoPET-III_BoxSize_Task02_Sagittal_Test", + dataset_name="autoPET-III", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="autoPET-III_BoxSize_Task02_Coronal_Train", + dataset_name="autoPET-III", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="autoPET-III_BoxSize_Task02_Coronal_Test", + dataset_name="autoPET-III", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="autoPET-III_BoxSize_Task02_Axial_Train", + dataset_name="autoPET-III", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="autoPET-III_BoxSize_Task02_Axial_Test", + dataset_name="autoPET-III", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # autoPET-III:Tumor-Lesion-Size:Task01 + MedVisionConfig( + name="autoPET-III_TumorLesionSize_Task01_Sagittal_Train", + dataset_name="autoPET-III", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="autoPET-III_TumorLesionSize_Task01_Sagittal_Test", + dataset_name="autoPET-III", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="autoPET-III_TumorLesionSize_Task01_Coronal_Train", + dataset_name="autoPET-III", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="autoPET-III_TumorLesionSize_Task01_Coronal_Test", + dataset_name="autoPET-III", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="autoPET-III_TumorLesionSize_Task01_Axial_Train", + dataset_name="autoPET-III", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="autoPET-III_TumorLesionSize_Task01_Axial_Test", + dataset_name="autoPET-III", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # BCV15:Mask-Size:Task01 + MedVisionConfig( + name="BCV15_MaskSize_Task01_Sagittal_Train", + dataset_name="BCV15", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BCV15_MaskSize_Task01_Sagittal_Test", + dataset_name="BCV15", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BCV15_MaskSize_Task01_Coronal_Train", + dataset_name="BCV15", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BCV15_MaskSize_Task01_Coronal_Test", + dataset_name="BCV15", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BCV15_MaskSize_Task01_Axial_Train", + dataset_name="BCV15", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BCV15_MaskSize_Task01_Axial_Test", + dataset_name="BCV15", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BCV15:Mask-Size:Task02 + MedVisionConfig( + name="BCV15_MaskSize_Task02_Sagittal_Train", + dataset_name="BCV15", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BCV15_MaskSize_Task02_Sagittal_Test", + dataset_name="BCV15", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BCV15_MaskSize_Task02_Coronal_Train", + dataset_name="BCV15", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BCV15_MaskSize_Task02_Coronal_Test", + dataset_name="BCV15", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BCV15_MaskSize_Task02_Axial_Train", + dataset_name="BCV15", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BCV15_MaskSize_Task02_Axial_Test", + dataset_name="BCV15", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BCV15:Box-Size:Task01 + MedVisionConfig( + name="BCV15_BoxSize_Task01_Sagittal_Train", + dataset_name="BCV15", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BCV15_BoxSize_Task01_Sagittal_Test", + dataset_name="BCV15", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BCV15_BoxSize_Task01_Coronal_Train", + dataset_name="BCV15", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BCV15_BoxSize_Task01_Coronal_Test", + dataset_name="BCV15", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BCV15_BoxSize_Task01_Axial_Train", + dataset_name="BCV15", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BCV15_BoxSize_Task01_Axial_Test", + dataset_name="BCV15", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BCV15:Box-Size:Task02 + MedVisionConfig( + name="BCV15_BoxSize_Task02_Sagittal_Train", + dataset_name="BCV15", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BCV15_BoxSize_Task02_Sagittal_Test", + dataset_name="BCV15", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BCV15_BoxSize_Task02_Coronal_Train", + dataset_name="BCV15", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BCV15_BoxSize_Task02_Coronal_Test", + dataset_name="BCV15", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BCV15_BoxSize_Task02_Axial_Train", + dataset_name="BCV15", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BCV15_BoxSize_Task02_Axial_Test", + dataset_name="BCV15", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Mask-Size:Task01 + MedVisionConfig( + name="BraTS24_MaskSize_Task01_Sagittal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task01_Sagittal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task01_Coronal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task01_Coronal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task01_Axial_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task01_Axial_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Mask-Size:Task02 + MedVisionConfig( + name="BraTS24_MaskSize_Task02_Sagittal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task02_Sagittal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task02_Coronal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task02_Coronal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task02_Axial_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task02_Axial_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Mask-Size:Task03 + MedVisionConfig( + name="BraTS24_MaskSize_Task03_Sagittal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task03_Sagittal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task03_Coronal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task03_Coronal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task03_Axial_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task03_Axial_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Mask-Size:Task04 + MedVisionConfig( + name="BraTS24_MaskSize_Task04_Sagittal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task04_Sagittal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task04_Coronal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task04_Coronal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task04_Axial_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task04_Axial_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Mask-Size:Task05 + MedVisionConfig( + name="BraTS24_MaskSize_Task05_Sagittal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task05_Sagittal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task05_Coronal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task05_Coronal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task05_Axial_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task05_Axial_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Mask-Size:Task06 + MedVisionConfig( + name="BraTS24_MaskSize_Task06_Sagittal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task06_Sagittal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task06_Coronal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task06_Coronal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task06_Axial_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task06_Axial_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Mask-Size:Task07 + MedVisionConfig( + name="BraTS24_MaskSize_Task07_Sagittal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task07_Sagittal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task07_Coronal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task07_Coronal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task07_Axial_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task07_Axial_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Mask-Size:Task08 + MedVisionConfig( + name="BraTS24_MaskSize_Task08_Sagittal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task08_Sagittal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task08_Coronal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task08_Coronal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task08_Axial_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task08_Axial_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Mask-Size:Task09 + MedVisionConfig( + name="BraTS24_MaskSize_Task09_Sagittal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task09_Sagittal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task09_Coronal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task09_Coronal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task09_Axial_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task09_Axial_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Mask-Size:Task10 + MedVisionConfig( + name="BraTS24_MaskSize_Task10_Sagittal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task10_Sagittal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task10_Coronal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task10_Coronal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task10_Axial_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task10_Axial_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Mask-Size:Task11 + MedVisionConfig( + name="BraTS24_MaskSize_Task11_Sagittal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task11_Sagittal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task11_Coronal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task11_Coronal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task11_Axial_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task11_Axial_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Mask-Size:Task12 + MedVisionConfig( + name="BraTS24_MaskSize_Task12_Sagittal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task12_Sagittal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task12_Coronal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task12_Coronal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task12_Axial_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task12_Axial_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Mask-Size:Task13 + MedVisionConfig( + name="BraTS24_MaskSize_Task13_Sagittal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task13_Sagittal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task13_Coronal_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task13_Coronal_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task13_Axial_Train", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_MaskSize_Task13_Axial_Test", + dataset_name="BraTS24", + taskType="Mask-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Box-Size:Task01 + MedVisionConfig( + name="BraTS24_BoxSize_Task01_Sagittal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task01_Sagittal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task01_Coronal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task01_Coronal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task01_Axial_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task01_Axial_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Box-Size:Task02 + MedVisionConfig( + name="BraTS24_BoxSize_Task02_Sagittal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task02_Sagittal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task02_Coronal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task02_Coronal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task02_Axial_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task02_Axial_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Box-Size:Task03 + MedVisionConfig( + name="BraTS24_BoxSize_Task03_Sagittal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task03_Sagittal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task03_Coronal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task03_Coronal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task03_Axial_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task03_Axial_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Box-Size:Task04 + MedVisionConfig( + name="BraTS24_BoxSize_Task04_Sagittal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task04_Sagittal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task04_Coronal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task04_Coronal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task04_Axial_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task04_Axial_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Box-Size:Task05 + MedVisionConfig( + name="BraTS24_BoxSize_Task05_Sagittal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task05_Sagittal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task05_Coronal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task05_Coronal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task05_Axial_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task05_Axial_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Box-Size:Task06 + MedVisionConfig( + name="BraTS24_BoxSize_Task06_Sagittal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task06_Sagittal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task06_Coronal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task06_Coronal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task06_Axial_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task06_Axial_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Box-Size:Task07 + MedVisionConfig( + name="BraTS24_BoxSize_Task07_Sagittal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task07_Sagittal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task07_Coronal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task07_Coronal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task07_Axial_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task07_Axial_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Box-Size:Task08 + MedVisionConfig( + name="BraTS24_BoxSize_Task08_Sagittal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task08_Sagittal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task08_Coronal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task08_Coronal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task08_Axial_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task08_Axial_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Box-Size:Task09 + MedVisionConfig( + name="BraTS24_BoxSize_Task09_Sagittal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task09_Sagittal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task09_Coronal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task09_Coronal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task09_Axial_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task09_Axial_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Box-Size:Task10 + MedVisionConfig( + name="BraTS24_BoxSize_Task10_Sagittal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task10_Sagittal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task10_Coronal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task10_Coronal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task10_Axial_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task10_Axial_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Box-Size:Task11 + MedVisionConfig( + name="BraTS24_BoxSize_Task11_Sagittal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task11_Sagittal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task11_Coronal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task11_Coronal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task11_Axial_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task11_Axial_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Box-Size:Task12 + MedVisionConfig( + name="BraTS24_BoxSize_Task12_Sagittal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task12_Sagittal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task12_Coronal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task12_Coronal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task12_Axial_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task12_Axial_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Box-Size:Task13 + MedVisionConfig( + name="BraTS24_BoxSize_Task13_Sagittal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task13_Sagittal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task13_Coronal_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task13_Coronal_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task13_Axial_Train", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_BoxSize_Task13_Axial_Test", + dataset_name="BraTS24", + taskType="Box-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Tumor-Lesion-Size:Task01 + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task01_Sagittal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task01_Sagittal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task01_Coronal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task01_Coronal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task01_Axial_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task01_Axial_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Tumor-Lesion-Size:Task02 + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task02_Sagittal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task02_Sagittal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task02_Coronal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task02_Coronal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task02_Axial_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task02_Axial_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Tumor-Lesion-Size:Task03 + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task03_Sagittal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task03_Sagittal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task03_Coronal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task03_Coronal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task03_Axial_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task03_Axial_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Tumor-Lesion-Size:Task04 + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task04_Sagittal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task04_Sagittal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task04_Coronal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task04_Coronal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task04_Axial_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task04_Axial_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Tumor-Lesion-Size:Task05 + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task05_Sagittal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task05_Sagittal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task05_Coronal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task05_Coronal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task05_Axial_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task05_Axial_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Tumor-Lesion-Size:Task06 + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task06_Sagittal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task06_Sagittal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task06_Coronal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task06_Coronal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task06_Axial_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task06_Axial_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Tumor-Lesion-Size:Task07 + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task07_Sagittal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task07_Sagittal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task07_Coronal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task07_Coronal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task07_Axial_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task07_Axial_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Tumor-Lesion-Size:Task08 + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task08_Sagittal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task08_Sagittal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task08_Coronal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task08_Coronal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task08_Axial_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task08_Axial_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Tumor-Lesion-Size:Task09 + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task09_Sagittal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task09_Sagittal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task09_Coronal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task09_Coronal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task09_Axial_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task09_Axial_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Tumor-Lesion-Size:Task10 + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task10_Sagittal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task10_Sagittal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task10_Coronal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task10_Coronal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task10_Axial_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task10_Axial_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Tumor-Lesion-Size:Task11 + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task11_Sagittal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task11_Sagittal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task11_Coronal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task11_Coronal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task11_Axial_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task11_Axial_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # BraTS24:Tumor-Lesion-Size:Task12 + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task12_Sagittal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task12_Sagittal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task12_Coronal_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task12_Coronal_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task12_Axial_Train", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="BraTS24_TumorLesionSize_Task12_Axial_Test", + dataset_name="BraTS24", + taskType="Tumor-Lesion-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # CAMUS:Mask-Size:Task01 + MedVisionConfig( + name="CAMUS_MaskSize_Task01_Sagittal_Train", + dataset_name="CAMUS", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="CAMUS_MaskSize_Task01_Sagittal_Test", + dataset_name="CAMUS", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="CAMUS_MaskSize_Task01_Coronal_Train", + dataset_name="CAMUS", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="CAMUS_MaskSize_Task01_Coronal_Test", + dataset_name="CAMUS", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="CAMUS_MaskSize_Task01_Axial_Train", + dataset_name="CAMUS", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="CAMUS_MaskSize_Task01_Axial_Test", + dataset_name="CAMUS", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # CAMUS:Box-Size:Task01 + MedVisionConfig( + name="CAMUS_BoxSize_Task01_Sagittal_Train", + dataset_name="CAMUS", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="CAMUS_BoxSize_Task01_Sagittal_Test", + dataset_name="CAMUS", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="CAMUS_BoxSize_Task01_Coronal_Train", + dataset_name="CAMUS", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="CAMUS_BoxSize_Task01_Coronal_Test", + dataset_name="CAMUS", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="CAMUS_BoxSize_Task01_Axial_Train", + dataset_name="CAMUS", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="CAMUS_BoxSize_Task01_Axial_Test", + dataset_name="CAMUS", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # Ceph-Biometrics-400:Biometrics-From-Landmarks:Task01 + # NOTE: We split angle and distance estimate tasks into different subsets + MedVisionConfig( + name="Ceph-Biometrics-400_BiometricsFromLandmarks_Distance_Task01_Sagittal_Train", + dataset_name="Ceph-Biometrics-400", + taskType="Biometrics-From-Landmarks-Distance", + taskID="01", + imageType="2D", + features_dict=features_dict_BiometricsFromLandmarks, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="Ceph-Biometrics-400_BiometricsFromLandmarks_Distance_Task01_Sagittal_Test", + dataset_name="Ceph-Biometrics-400", + taskType="Biometrics-From-Landmarks-Distance", + taskID="01", + imageType="2D", + features_dict=features_dict_BiometricsFromLandmarks, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="Ceph-Biometrics-400_BiometricsFromLandmarks_Angle_Task01_Sagittal_Train", + dataset_name="Ceph-Biometrics-400", + taskType="Biometrics-From-Landmarks-Angle", + taskID="01", + imageType="2D", + features_dict=features_dict_BiometricsFromLandmarks, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="Ceph-Biometrics-400_BiometricsFromLandmarks_Angle_Task01_Sagittal_Test", + dataset_name="Ceph-Biometrics-400", + taskType="Biometrics-From-Landmarks-Angle", + taskID="01", + imageType="2D", + features_dict=features_dict_BiometricsFromLandmarks, + imageSliceType="sagittal", + split="test", + ), + # CrossMoDA:Mask-Size:Task01 + MedVisionConfig( + name="CrossMoDA_MaskSize_Task01_Sagittal_Train", + dataset_name="CrossMoDA", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="CrossMoDA_MaskSize_Task01_Sagittal_Test", + dataset_name="CrossMoDA", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="CrossMoDA_MaskSize_Task01_Coronal_Train", + dataset_name="CrossMoDA", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="CrossMoDA_MaskSize_Task01_Coronal_Test", + dataset_name="CrossMoDA", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="CrossMoDA_MaskSize_Task01_Axial_Train", + dataset_name="CrossMoDA", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="CrossMoDA_MaskSize_Task01_Axial_Test", + dataset_name="CrossMoDA", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # CrossMoDA:Box-Size:Task01 + MedVisionConfig( + name="CrossMoDA_BoxSize_Task01_Sagittal_Train", + dataset_name="CrossMoDA", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="CrossMoDA_BoxSize_Task01_Sagittal_Test", + dataset_name="CrossMoDA", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="CrossMoDA_BoxSize_Task01_Coronal_Train", + dataset_name="CrossMoDA", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="CrossMoDA_BoxSize_Task01_Coronal_Test", + dataset_name="CrossMoDA", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="CrossMoDA_BoxSize_Task01_Axial_Train", + dataset_name="CrossMoDA", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="CrossMoDA_BoxSize_Task01_Axial_Test", + dataset_name="CrossMoDA", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # FeTA24:Mask-Size:Task01 + MedVisionConfig( + name="FeTA24_MaskSize_Task01_Sagittal_Train", + dataset_name="FeTA24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="FeTA24_MaskSize_Task01_Sagittal_Test", + dataset_name="FeTA24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="FeTA24_MaskSize_Task01_Coronal_Train", + dataset_name="FeTA24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="FeTA24_MaskSize_Task01_Coronal_Test", + dataset_name="FeTA24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="FeTA24_MaskSize_Task01_Axial_Train", + dataset_name="FeTA24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="FeTA24_MaskSize_Task01_Axial_Test", + dataset_name="FeTA24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # FeTA24:Box-Size:Task01 + MedVisionConfig( + name="FeTA24_BoxSize_Task01_Sagittal_Train", + dataset_name="FeTA24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="FeTA24_BoxSize_Task01_Sagittal_Test", + dataset_name="FeTA24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="FeTA24_BoxSize_Task01_Coronal_Train", + dataset_name="FeTA24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="FeTA24_BoxSize_Task01_Coronal_Test", + dataset_name="FeTA24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="FeTA24_BoxSize_Task01_Axial_Train", + dataset_name="FeTA24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="FeTA24_BoxSize_Task01_Axial_Test", + dataset_name="FeTA24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # FeTA24:Biometrics-From-Landmarks:Task01 + MedVisionConfig( + name="FeTA24_BiometricsFromLandmarks_Task01_Sagittal_Train", + dataset_name="FeTA24", + taskType="Biometrics-From-Landmarks", + taskID="01", + imageType="2D", + features_dict=features_dict_BiometricsFromLandmarks, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="FeTA24_BiometricsFromLandmarks_Task01_Sagittal_Test", + dataset_name="FeTA24", + taskType="Biometrics-From-Landmarks", + taskID="01", + imageType="2D", + features_dict=features_dict_BiometricsFromLandmarks, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="FeTA24_BiometricsFromLandmarks_Task01_Coronal_Train", + dataset_name="FeTA24", + taskType="Biometrics-From-Landmarks", + taskID="01", + imageType="2D", + features_dict=features_dict_BiometricsFromLandmarks, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="FeTA24_BiometricsFromLandmarks_Task01_Coronal_Test", + dataset_name="FeTA24", + taskType="Biometrics-From-Landmarks", + taskID="01", + imageType="2D", + features_dict=features_dict_BiometricsFromLandmarks, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="FeTA24_BiometricsFromLandmarks_Task01_Axial_Train", + dataset_name="FeTA24", + taskType="Biometrics-From-Landmarks", + taskID="01", + imageType="2D", + features_dict=features_dict_BiometricsFromLandmarks, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="FeTA24_BiometricsFromLandmarks_Task01_Axial_Test", + dataset_name="FeTA24", + taskType="Biometrics-From-Landmarks", + taskID="01", + imageType="2D", + features_dict=features_dict_BiometricsFromLandmarks, + imageSliceType="axial", + split="test", + ), + # FLARE22:Mask-Size:Task01 + MedVisionConfig( + name="FLARE22_MaskSize_Task01_Sagittal_Train", + dataset_name="FLARE22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="FLARE22_MaskSize_Task01_Sagittal_Test", + dataset_name="FLARE22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="FLARE22_MaskSize_Task01_Coronal_Train", + dataset_name="FLARE22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="FLARE22_MaskSize_Task01_Coronal_Test", + dataset_name="FLARE22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="FLARE22_MaskSize_Task01_Axial_Train", + dataset_name="FLARE22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="FLARE22_MaskSize_Task01_Axial_Test", + dataset_name="FLARE22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # FLARE22:Box-Size:Task01 + MedVisionConfig( + name="FLARE22_BoxSize_Task01_Sagittal_Train", + dataset_name="FLARE22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="FLARE22_BoxSize_Task01_Sagittal_Test", + dataset_name="FLARE22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="FLARE22_BoxSize_Task01_Coronal_Train", + dataset_name="FLARE22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="FLARE22_BoxSize_Task01_Coronal_Test", + dataset_name="FLARE22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="FLARE22_BoxSize_Task01_Axial_Train", + dataset_name="FLARE22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="FLARE22_BoxSize_Task01_Axial_Test", + dataset_name="FLARE22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # HNTSMRG24:Mask-Size:Task01 + MedVisionConfig( + name="HNTSMRG24_MaskSize_Task01_Sagittal_Train", + dataset_name="HNTSMRG24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_MaskSize_Task01_Sagittal_Test", + dataset_name="HNTSMRG24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_MaskSize_Task01_Coronal_Train", + dataset_name="HNTSMRG24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_MaskSize_Task01_Coronal_Test", + dataset_name="HNTSMRG24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_MaskSize_Task01_Axial_Train", + dataset_name="HNTSMRG24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_MaskSize_Task01_Axial_Test", + dataset_name="HNTSMRG24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # HNTSMRG24:Mask-Size:Task02 + MedVisionConfig( + name="HNTSMRG24_MaskSize_Task02_Sagittal_Train", + dataset_name="HNTSMRG24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_MaskSize_Task02_Sagittal_Test", + dataset_name="HNTSMRG24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_MaskSize_Task02_Coronal_Train", + dataset_name="HNTSMRG24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_MaskSize_Task02_Coronal_Test", + dataset_name="HNTSMRG24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_MaskSize_Task02_Axial_Train", + dataset_name="HNTSMRG24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_MaskSize_Task02_Axial_Test", + dataset_name="HNTSMRG24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # HNTSMRG24:Box-Size:Task01 + MedVisionConfig( + name="HNTSMRG24_BoxSize_Task01_Sagittal_Train", + dataset_name="HNTSMRG24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_BoxSize_Task01_Sagittal_Test", + dataset_name="HNTSMRG24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_BoxSize_Task01_Coronal_Train", + dataset_name="HNTSMRG24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_BoxSize_Task01_Coronal_Test", + dataset_name="HNTSMRG24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_BoxSize_Task01_Axial_Train", + dataset_name="HNTSMRG24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_BoxSize_Task01_Axial_Test", + dataset_name="HNTSMRG24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # HNTSMRG24:Box-Size:Task02 + MedVisionConfig( + name="HNTSMRG24_BoxSize_Task02_Sagittal_Train", + dataset_name="HNTSMRG24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_BoxSize_Task02_Sagittal_Test", + dataset_name="HNTSMRG24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_BoxSize_Task02_Coronal_Train", + dataset_name="HNTSMRG24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_BoxSize_Task02_Coronal_Test", + dataset_name="HNTSMRG24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_BoxSize_Task02_Axial_Train", + dataset_name="HNTSMRG24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_BoxSize_Task02_Axial_Test", + dataset_name="HNTSMRG24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # HNTSMRG24:Tumor-Lesion-Size:Task01 + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task01_Sagittal_Train", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task01_Sagittal_Test", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task01_Coronal_Train", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task01_Coronal_Test", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task01_Axial_Train", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task01_Axial_Test", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # HNTSMRG24:Tumor-Lesion-Size:Task02 + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task02_Sagittal_Train", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task02_Sagittal_Test", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task02_Coronal_Train", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task02_Coronal_Test", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task02_Axial_Train", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task02_Axial_Test", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # HNTSMRG24:Tumor-Lesion-Size:Task03 + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task03_Sagittal_Train", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task03_Sagittal_Test", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task03_Coronal_Train", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task03_Coronal_Test", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task03_Axial_Train", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task03_Axial_Test", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # HNTSMRG24:Tumor-Lesion-Size:Task04 + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task04_Sagittal_Train", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task04_Sagittal_Test", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task04_Coronal_Train", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task04_Coronal_Test", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task04_Axial_Train", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="HNTSMRG24_TumorLesionSize_Task04_Axial_Test", + dataset_name="HNTSMRG24", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # ISLES24:Mask-Size:Task01 + MedVisionConfig( + name="ISLES24_MaskSize_Task01_Sagittal_Train", + dataset_name="ISLES24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="ISLES24_MaskSize_Task01_Sagittal_Test", + dataset_name="ISLES24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="ISLES24_MaskSize_Task01_Coronal_Train", + dataset_name="ISLES24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="ISLES24_MaskSize_Task01_Coronal_Test", + dataset_name="ISLES24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="ISLES24_MaskSize_Task01_Axial_Train", + dataset_name="ISLES24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="ISLES24_MaskSize_Task01_Axial_Test", + dataset_name="ISLES24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # ISLES24:Mask-Size:Task02 + MedVisionConfig( + name="ISLES24_MaskSize_Task02_Sagittal_Train", + dataset_name="ISLES24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="ISLES24_MaskSize_Task02_Sagittal_Test", + dataset_name="ISLES24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="ISLES24_MaskSize_Task02_Coronal_Train", + dataset_name="ISLES24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="ISLES24_MaskSize_Task02_Coronal_Test", + dataset_name="ISLES24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="ISLES24_MaskSize_Task02_Axial_Train", + dataset_name="ISLES24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="ISLES24_MaskSize_Task02_Axial_Test", + dataset_name="ISLES24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # ISLES24:Box-Size:Task01 + MedVisionConfig( + name="ISLES24_BoxSize_Task01_Sagittal_Train", + dataset_name="ISLES24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="ISLES24_BoxSize_Task01_Sagittal_Test", + dataset_name="ISLES24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="ISLES24_BoxSize_Task01_Coronal_Train", + dataset_name="ISLES24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="ISLES24_BoxSize_Task01_Coronal_Test", + dataset_name="ISLES24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="ISLES24_BoxSize_Task01_Axial_Train", + dataset_name="ISLES24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="ISLES24_BoxSize_Task01_Axial_Test", + dataset_name="ISLES24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # ISLES24:Box-Size:Task02 + MedVisionConfig( + name="ISLES24_BoxSize_Task02_Sagittal_Train", + dataset_name="ISLES24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="ISLES24_BoxSize_Task02_Sagittal_Test", + dataset_name="ISLES24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="ISLES24_BoxSize_Task02_Coronal_Train", + dataset_name="ISLES24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="ISLES24_BoxSize_Task02_Coronal_Test", + dataset_name="ISLES24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="ISLES24_BoxSize_Task02_Axial_Train", + dataset_name="ISLES24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="ISLES24_BoxSize_Task02_Axial_Test", + dataset_name="ISLES24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # KiPA22:Mask-Size:Task01 + MedVisionConfig( + name="KiPA22_MaskSize_Task01_Sagittal_Train", + dataset_name="KiPA22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="KiPA22_MaskSize_Task01_Sagittal_Test", + dataset_name="KiPA22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="KiPA22_MaskSize_Task01_Coronal_Train", + dataset_name="KiPA22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="KiPA22_MaskSize_Task01_Coronal_Test", + dataset_name="KiPA22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="KiPA22_MaskSize_Task01_Axial_Train", + dataset_name="KiPA22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="KiPA22_MaskSize_Task01_Axial_Test", + dataset_name="KiPA22", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # KiPA22:Box-Size:Task01 + MedVisionConfig( + name="KiPA22_BoxSize_Task01_Sagittal_Train", + dataset_name="KiPA22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="KiPA22_BoxSize_Task01_Sagittal_Test", + dataset_name="KiPA22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="KiPA22_BoxSize_Task01_Coronal_Train", + dataset_name="KiPA22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="KiPA22_BoxSize_Task01_Coronal_Test", + dataset_name="KiPA22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="KiPA22_BoxSize_Task01_Axial_Train", + dataset_name="KiPA22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="KiPA22_BoxSize_Task01_Axial_Test", + dataset_name="KiPA22", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # KiPA22:Tumor-Lesion-Size:Task01 + MedVisionConfig( + name="KiPA22_TumorLesionSize_Task01_Sagittal_Train", + dataset_name="KiPA22", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="KiPA22_TumorLesionSize_Task01_Sagittal_Test", + dataset_name="KiPA22", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="KiPA22_TumorLesionSize_Task01_Coronal_Train", + dataset_name="KiPA22", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="KiPA22_TumorLesionSize_Task01_Coronal_Test", + dataset_name="KiPA22", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="KiPA22_TumorLesionSize_Task01_Axial_Train", + dataset_name="KiPA22", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="KiPA22_TumorLesionSize_Task01_Axial_Test", + dataset_name="KiPA22", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # KiTS23:Mask-Size:Task01 + MedVisionConfig( + name="KiTS23_MaskSize_Task01_Sagittal_Train", + dataset_name="KiTS23", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="KiTS23_MaskSize_Task01_Sagittal_Test", + dataset_name="KiTS23", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="KiTS23_MaskSize_Task01_Coronal_Train", + dataset_name="KiTS23", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="KiTS23_MaskSize_Task01_Coronal_Test", + dataset_name="KiTS23", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="KiTS23_MaskSize_Task01_Axial_Train", + dataset_name="KiTS23", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="KiTS23_MaskSize_Task01_Axial_Test", + dataset_name="KiTS23", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # KiTS23:Box-Size:Task01 + MedVisionConfig( + name="KiTS23_BoxSize_Task01_Sagittal_Train", + dataset_name="KiTS23", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="KiTS23_BoxSize_Task01_Sagittal_Test", + dataset_name="KiTS23", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="KiTS23_BoxSize_Task01_Coronal_Train", + dataset_name="KiTS23", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="KiTS23_BoxSize_Task01_Coronal_Test", + dataset_name="KiTS23", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="KiTS23_BoxSize_Task01_Axial_Train", + dataset_name="KiTS23", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="KiTS23_BoxSize_Task01_Axial_Test", + dataset_name="KiTS23", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # KiTS23:Tumor-Lesion-Size:Task01 + MedVisionConfig( + name="KiTS23_TumorLesionSize_Task01_Sagittal_Train", + dataset_name="KiTS23", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="KiTS23_TumorLesionSize_Task01_Sagittal_Test", + dataset_name="KiTS23", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="KiTS23_TumorLesionSize_Task01_Coronal_Train", + dataset_name="KiTS23", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="KiTS23_TumorLesionSize_Task01_Coronal_Test", + dataset_name="KiTS23", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="KiTS23_TumorLesionSize_Task01_Axial_Train", + dataset_name="KiTS23", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="KiTS23_TumorLesionSize_Task01_Axial_Test", + dataset_name="KiTS23", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # MSD:Mask-Size:Task01 + MedVisionConfig( + name="MSD_MaskSize_Task01_Sagittal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task01_Sagittal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task01_Coronal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task01_Coronal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task01_Axial_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task01_Axial_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # MSD:Mask-Size:Task02 + MedVisionConfig( + name="MSD_MaskSize_Task02_Sagittal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task02_Sagittal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task02_Coronal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task02_Coronal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task02_Axial_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task02_Axial_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # MSD:Mask-Size:Task03 + MedVisionConfig( + name="MSD_MaskSize_Task03_Sagittal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task03_Sagittal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task03_Coronal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task03_Coronal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task03_Axial_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task03_Axial_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # MSD:Mask-Size:Task04 + MedVisionConfig( + name="MSD_MaskSize_Task04_Sagittal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task04_Sagittal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task04_Coronal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task04_Coronal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task04_Axial_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task04_Axial_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # MSD:Mask-Size:Task05 + MedVisionConfig( + name="MSD_MaskSize_Task05_Sagittal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task05_Sagittal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task05_Coronal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task05_Coronal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task05_Axial_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task05_Axial_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # MSD:Mask-Size:Task06 + MedVisionConfig( + name="MSD_MaskSize_Task06_Sagittal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task06_Sagittal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task06_Coronal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task06_Coronal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task06_Axial_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task06_Axial_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # MSD:Mask-Size:Task07 + MedVisionConfig( + name="MSD_MaskSize_Task07_Sagittal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task07_Sagittal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task07_Coronal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task07_Coronal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task07_Axial_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task07_Axial_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # MSD:Mask-Size:Task08 + MedVisionConfig( + name="MSD_MaskSize_Task08_Sagittal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task08_Sagittal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task08_Coronal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task08_Coronal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task08_Axial_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task08_Axial_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # MSD:Mask-Size:Task09 + MedVisionConfig( + name="MSD_MaskSize_Task09_Sagittal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task09_Sagittal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task09_Coronal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task09_Coronal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task09_Axial_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task09_Axial_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # MSD:Mask-Size:Task10 + MedVisionConfig( + name="MSD_MaskSize_Task10_Sagittal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task10_Sagittal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task10_Coronal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task10_Coronal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task10_Axial_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task10_Axial_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # MSD:Mask-Size:Task11 + MedVisionConfig( + name="MSD_MaskSize_Task11_Sagittal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task11_Sagittal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task11_Coronal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task11_Coronal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task11_Axial_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task11_Axial_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # MSD:Mask-Size:Task12 + MedVisionConfig( + name="MSD_MaskSize_Task12_Sagittal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task12_Sagittal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task12_Coronal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task12_Coronal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task12_Axial_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task12_Axial_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # MSD:Mask-Size:Task13 + MedVisionConfig( + name="MSD_MaskSize_Task13_Sagittal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task13_Sagittal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task13_Coronal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task13_Coronal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task13_Axial_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task13_Axial_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # MSD:Mask-Size:Task14 + MedVisionConfig( + name="MSD_MaskSize_Task14_Sagittal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="14", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task14_Sagittal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="14", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task14_Coronal_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="14", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task14_Coronal_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="14", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_MaskSize_Task14_Axial_Train", + dataset_name="MSD", + taskType="Mask-Size", + taskID="14", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_MaskSize_Task14_Axial_Test", + dataset_name="MSD", + taskType="Mask-Size", + taskID="14", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # MSD:Box-Size:Task01 + MedVisionConfig( + name="MSD_BoxSize_Task01_Sagittal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task01_Sagittal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task01_Coronal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task01_Coronal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task01_Axial_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task01_Axial_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # MSD:Box-Size:Task02 + MedVisionConfig( + name="MSD_BoxSize_Task02_Sagittal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task02_Sagittal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task02_Coronal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task02_Coronal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task02_Axial_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task02_Axial_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # MSD:Box-Size:Task03 + MedVisionConfig( + name="MSD_BoxSize_Task03_Sagittal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task03_Sagittal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task03_Coronal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task03_Coronal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task03_Axial_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task03_Axial_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # MSD:Box-Size:Task04 + MedVisionConfig( + name="MSD_BoxSize_Task04_Sagittal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task04_Sagittal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task04_Coronal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task04_Coronal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task04_Axial_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task04_Axial_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # MSD:Box-Size:Task05 + MedVisionConfig( + name="MSD_BoxSize_Task05_Sagittal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task05_Sagittal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task05_Coronal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task05_Coronal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task05_Axial_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task05_Axial_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # MSD:Box-Size:Task06 + MedVisionConfig( + name="MSD_BoxSize_Task06_Sagittal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task06_Sagittal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task06_Coronal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task06_Coronal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task06_Axial_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task06_Axial_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # MSD:Box-Size:Task07 + MedVisionConfig( + name="MSD_BoxSize_Task07_Sagittal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task07_Sagittal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task07_Coronal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task07_Coronal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task07_Axial_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task07_Axial_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # MSD:Box-Size:Task08 + MedVisionConfig( + name="MSD_BoxSize_Task08_Sagittal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task08_Sagittal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task08_Coronal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task08_Coronal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task08_Axial_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task08_Axial_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # MSD:Box-Size:Task09 + MedVisionConfig( + name="MSD_BoxSize_Task09_Sagittal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task09_Sagittal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task09_Coronal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task09_Coronal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task09_Axial_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task09_Axial_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="09", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # MSD:Box-Size:Task10 + MedVisionConfig( + name="MSD_BoxSize_Task10_Sagittal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task10_Sagittal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task10_Coronal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task10_Coronal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task10_Axial_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task10_Axial_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="10", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # MSD:Box-Size:Task11 + MedVisionConfig( + name="MSD_BoxSize_Task11_Sagittal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task11_Sagittal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task11_Coronal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task11_Coronal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task11_Axial_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task11_Axial_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="11", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # MSD:Box-Size:Task12 + MedVisionConfig( + name="MSD_BoxSize_Task12_Sagittal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task12_Sagittal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task12_Coronal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task12_Coronal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task12_Axial_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task12_Axial_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="12", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # MSD:Box-Size:Task13 + MedVisionConfig( + name="MSD_BoxSize_Task13_Sagittal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task13_Sagittal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task13_Coronal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task13_Coronal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task13_Axial_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task13_Axial_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="13", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # MSD:Box-Size:Task14 + MedVisionConfig( + name="MSD_BoxSize_Task14_Sagittal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="14", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task14_Sagittal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="14", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task14_Coronal_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="14", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task14_Coronal_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="14", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_BoxSize_Task14_Axial_Train", + dataset_name="MSD", + taskType="Box-Size", + taskID="14", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_BoxSize_Task14_Axial_Test", + dataset_name="MSD", + taskType="Box-Size", + taskID="14", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # MSD:Tumor-Lesion-Size:Task01 + MedVisionConfig( + name="MSD_TumorLesionSize_Task01_Sagittal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task01_Sagittal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task01_Coronal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task01_Coronal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task01_Axial_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task01_Axial_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # MSD:Tumor-Lesion-Size:Task02 + MedVisionConfig( + name="MSD_TumorLesionSize_Task02_Sagittal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task02_Sagittal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task02_Coronal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task02_Coronal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task02_Axial_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task02_Axial_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # MSD:Tumor-Lesion-Size:Task03 + MedVisionConfig( + name="MSD_TumorLesionSize_Task03_Sagittal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task03_Sagittal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task03_Coronal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task03_Coronal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task03_Axial_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task03_Axial_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="03", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # MSD:Tumor-Lesion-Size:Task04 + MedVisionConfig( + name="MSD_TumorLesionSize_Task04_Sagittal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task04_Sagittal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task04_Coronal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task04_Coronal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task04_Axial_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task04_Axial_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="04", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # MSD:Tumor-Lesion-Size:Task05 + MedVisionConfig( + name="MSD_TumorLesionSize_Task05_Sagittal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task05_Sagittal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task05_Coronal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task05_Coronal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task05_Axial_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task05_Axial_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="05", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # MSD:Tumor-Lesion-Size:Task06 + MedVisionConfig( + name="MSD_TumorLesionSize_Task06_Sagittal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task06_Sagittal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task06_Coronal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task06_Coronal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task06_Axial_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task06_Axial_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="06", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # MSD:Tumor-Lesion-Size:Task07 + MedVisionConfig( + name="MSD_TumorLesionSize_Task07_Sagittal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task07_Sagittal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task07_Coronal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task07_Coronal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task07_Axial_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task07_Axial_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="07", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # MSD:Tumor-Lesion-Size:Task08 + MedVisionConfig( + name="MSD_TumorLesionSize_Task08_Sagittal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task08_Sagittal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task08_Coronal_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task08_Coronal_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task08_Axial_Train", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="MSD_TumorLesionSize_Task08_Axial_Test", + dataset_name="MSD", + taskType="Tumor-Lesion-Size", + taskID="08", + imageType="2D", + features_dict=features_dict_TumorLesionSize, + imageSliceType="axial", + split="test", + ), + # OAIZIB-CM:Mask-Size:Task01 + MedVisionConfig( + name="OAIZIB-CM_MaskSize_Task01_Sagittal_Train", + dataset_name="OAIZIB-CM", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="OAIZIB-CM_MaskSize_Task01_Sagittal_Test", + dataset_name="OAIZIB-CM", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="OAIZIB-CM_MaskSize_Task01_Coronal_Train", + dataset_name="OAIZIB-CM", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="OAIZIB-CM_MaskSize_Task01_Coronal_Test", + dataset_name="OAIZIB-CM", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="OAIZIB-CM_MaskSize_Task01_Axial_Train", + dataset_name="OAIZIB-CM", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="OAIZIB-CM_MaskSize_Task01_Axial_Test", + dataset_name="OAIZIB-CM", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # OAIZIB-CM:Box-Size:Task01 + MedVisionConfig( + name="OAIZIB-CM_BoxSize_Task01_Sagittal_Train", + dataset_name="OAIZIB-CM", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="OAIZIB-CM_BoxSize_Task01_Sagittal_Test", + dataset_name="OAIZIB-CM", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="OAIZIB-CM_BoxSize_Task01_Coronal_Train", + dataset_name="OAIZIB-CM", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="OAIZIB-CM_BoxSize_Task01_Coronal_Test", + dataset_name="OAIZIB-CM", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="OAIZIB-CM_BoxSize_Task01_Axial_Train", + dataset_name="OAIZIB-CM", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="OAIZIB-CM_BoxSize_Task01_Axial_Test", + dataset_name="OAIZIB-CM", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # SKM-TEA:Mask-Size:Task01 + MedVisionConfig( + name="SKM-TEA_MaskSize_Task01_Sagittal_Train", + dataset_name="SKM-TEA", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="SKM-TEA_MaskSize_Task01_Sagittal_Test", + dataset_name="SKM-TEA", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="SKM-TEA_MaskSize_Task01_Coronal_Train", + dataset_name="SKM-TEA", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="SKM-TEA_MaskSize_Task01_Coronal_Test", + dataset_name="SKM-TEA", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="SKM-TEA_MaskSize_Task01_Axial_Train", + dataset_name="SKM-TEA", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="SKM-TEA_MaskSize_Task01_Axial_Test", + dataset_name="SKM-TEA", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # SKM-TEA:Mask-Size:Task02 + MedVisionConfig( + name="SKM-TEA_MaskSize_Task02_Sagittal_Train", + dataset_name="SKM-TEA", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="SKM-TEA_MaskSize_Task02_Sagittal_Test", + dataset_name="SKM-TEA", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="SKM-TEA_MaskSize_Task02_Coronal_Train", + dataset_name="SKM-TEA", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="SKM-TEA_MaskSize_Task02_Coronal_Test", + dataset_name="SKM-TEA", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="SKM-TEA_MaskSize_Task02_Axial_Train", + dataset_name="SKM-TEA", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="SKM-TEA_MaskSize_Task02_Axial_Test", + dataset_name="SKM-TEA", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # SKM-TEA:Box-Size:Task01 + MedVisionConfig( + name="SKM-TEA_BoxSize_Task01_Sagittal_Train", + dataset_name="SKM-TEA", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="SKM-TEA_BoxSize_Task01_Sagittal_Test", + dataset_name="SKM-TEA", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="SKM-TEA_BoxSize_Task01_Coronal_Train", + dataset_name="SKM-TEA", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="SKM-TEA_BoxSize_Task01_Coronal_Test", + dataset_name="SKM-TEA", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="SKM-TEA_BoxSize_Task01_Axial_Train", + dataset_name="SKM-TEA", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="SKM-TEA_BoxSize_Task01_Axial_Test", + dataset_name="SKM-TEA", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # SKM-TEA:Box-Size:Task02 + MedVisionConfig( + name="SKM-TEA_BoxSize_Task02_Sagittal_Train", + dataset_name="SKM-TEA", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="SKM-TEA_BoxSize_Task02_Sagittal_Test", + dataset_name="SKM-TEA", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="SKM-TEA_BoxSize_Task02_Coronal_Train", + dataset_name="SKM-TEA", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="SKM-TEA_BoxSize_Task02_Coronal_Test", + dataset_name="SKM-TEA", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="SKM-TEA_BoxSize_Task02_Axial_Train", + dataset_name="SKM-TEA", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="SKM-TEA_BoxSize_Task02_Axial_Test", + dataset_name="SKM-TEA", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # ToothFairy2:Mask-Size:Task01 + MedVisionConfig( + name="ToothFairy2_MaskSize_Task01_Sagittal_Train", + dataset_name="ToothFairy2", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="ToothFairy2_MaskSize_Task01_Sagittal_Test", + dataset_name="ToothFairy2", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="ToothFairy2_MaskSize_Task01_Coronal_Train", + dataset_name="ToothFairy2", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="ToothFairy2_MaskSize_Task01_Coronal_Test", + dataset_name="ToothFairy2", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="ToothFairy2_MaskSize_Task01_Axial_Train", + dataset_name="ToothFairy2", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="ToothFairy2_MaskSize_Task01_Axial_Test", + dataset_name="ToothFairy2", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # ToothFairy2:Box-Size:Task01 + MedVisionConfig( + name="ToothFairy2_BoxSize_Task01_Sagittal_Train", + dataset_name="ToothFairy2", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="ToothFairy2_BoxSize_Task01_Sagittal_Test", + dataset_name="ToothFairy2", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="ToothFairy2_BoxSize_Task01_Coronal_Train", + dataset_name="ToothFairy2", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="ToothFairy2_BoxSize_Task01_Coronal_Test", + dataset_name="ToothFairy2", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="ToothFairy2_BoxSize_Task01_Axial_Train", + dataset_name="ToothFairy2", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="ToothFairy2_BoxSize_Task01_Axial_Test", + dataset_name="ToothFairy2", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # TopCoW24:Mask-Size:Task01 + MedVisionConfig( + name="TopCoW24_MaskSize_Task01_Sagittal_Train", + dataset_name="TopCoW24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="TopCoW24_MaskSize_Task01_Sagittal_Test", + dataset_name="TopCoW24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="TopCoW24_MaskSize_Task01_Coronal_Train", + dataset_name="TopCoW24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="TopCoW24_MaskSize_Task01_Coronal_Test", + dataset_name="TopCoW24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="TopCoW24_MaskSize_Task01_Axial_Train", + dataset_name="TopCoW24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="TopCoW24_MaskSize_Task01_Axial_Test", + dataset_name="TopCoW24", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # TopCoW24:Mask-Size:Task02 + MedVisionConfig( + name="TopCoW24_MaskSize_Task02_Sagittal_Train", + dataset_name="TopCoW24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="TopCoW24_MaskSize_Task02_Sagittal_Test", + dataset_name="TopCoW24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="TopCoW24_MaskSize_Task02_Coronal_Train", + dataset_name="TopCoW24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="TopCoW24_MaskSize_Task02_Coronal_Test", + dataset_name="TopCoW24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="TopCoW24_MaskSize_Task02_Axial_Train", + dataset_name="TopCoW24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="TopCoW24_MaskSize_Task02_Axial_Test", + dataset_name="TopCoW24", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # TopCoW24:Box-Size:Task01 + MedVisionConfig( + name="TopCoW24_BoxSize_Task01_Sagittal_Train", + dataset_name="TopCoW24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="TopCoW24_BoxSize_Task01_Sagittal_Test", + dataset_name="TopCoW24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="TopCoW24_BoxSize_Task01_Coronal_Train", + dataset_name="TopCoW24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="TopCoW24_BoxSize_Task01_Coronal_Test", + dataset_name="TopCoW24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="TopCoW24_BoxSize_Task01_Axial_Train", + dataset_name="TopCoW24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="TopCoW24_BoxSize_Task01_Axial_Test", + dataset_name="TopCoW24", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # TopCoW24:Box-Size:Task02 + MedVisionConfig( + name="TopCoW24_BoxSize_Task02_Sagittal_Train", + dataset_name="TopCoW24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="TopCoW24_BoxSize_Task02_Sagittal_Test", + dataset_name="TopCoW24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="TopCoW24_BoxSize_Task02_Coronal_Train", + dataset_name="TopCoW24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="TopCoW24_BoxSize_Task02_Coronal_Test", + dataset_name="TopCoW24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="TopCoW24_BoxSize_Task02_Axial_Train", + dataset_name="TopCoW24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="TopCoW24_BoxSize_Task02_Axial_Test", + dataset_name="TopCoW24", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # TotalSegmentator:Mask-Size:Task01 + MedVisionConfig( + name="TotalSegmentator_MaskSize_Task01_Sagittal_Train", + dataset_name="TotalSegmentator", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="TotalSegmentator_MaskSize_Task01_Sagittal_Test", + dataset_name="TotalSegmentator", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="TotalSegmentator_MaskSize_Task01_Coronal_Train", + dataset_name="TotalSegmentator", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="TotalSegmentator_MaskSize_Task01_Coronal_Test", + dataset_name="TotalSegmentator", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="TotalSegmentator_MaskSize_Task01_Axial_Train", + dataset_name="TotalSegmentator", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="TotalSegmentator_MaskSize_Task01_Axial_Test", + dataset_name="TotalSegmentator", + taskType="Mask-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # TotalSegmentator:Mask-Size:Task02 + MedVisionConfig( + name="TotalSegmentator_MaskSize_Task02_Sagittal_Train", + dataset_name="TotalSegmentator", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="TotalSegmentator_MaskSize_Task02_Sagittal_Test", + dataset_name="TotalSegmentator", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="TotalSegmentator_MaskSize_Task02_Coronal_Train", + dataset_name="TotalSegmentator", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="TotalSegmentator_MaskSize_Task02_Coronal_Test", + dataset_name="TotalSegmentator", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="TotalSegmentator_MaskSize_Task02_Axial_Train", + dataset_name="TotalSegmentator", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="TotalSegmentator_MaskSize_Task02_Axial_Test", + dataset_name="TotalSegmentator", + taskType="Mask-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_MaskSize, + imageSliceType="axial", + split="test", + ), + # TotalSegmentator:Box-Size:Task01 + MedVisionConfig( + name="TotalSegmentator_BoxSize_Task01_Sagittal_Train", + dataset_name="TotalSegmentator", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="TotalSegmentator_BoxSize_Task01_Sagittal_Test", + dataset_name="TotalSegmentator", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="TotalSegmentator_BoxSize_Task01_Coronal_Train", + dataset_name="TotalSegmentator", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="TotalSegmentator_BoxSize_Task01_Coronal_Test", + dataset_name="TotalSegmentator", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="TotalSegmentator_BoxSize_Task01_Axial_Train", + dataset_name="TotalSegmentator", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="TotalSegmentator_BoxSize_Task01_Axial_Test", + dataset_name="TotalSegmentator", + taskType="Box-Size", + taskID="01", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + # TotalSegmentator:Box-Size:Task02 + MedVisionConfig( + name="TotalSegmentator_BoxSize_Task02_Sagittal_Train", + dataset_name="TotalSegmentator", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="train", + ), + MedVisionConfig( + name="TotalSegmentator_BoxSize_Task02_Sagittal_Test", + dataset_name="TotalSegmentator", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="sagittal", + split="test", + ), + MedVisionConfig( + name="TotalSegmentator_BoxSize_Task02_Coronal_Train", + dataset_name="TotalSegmentator", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="train", + ), + MedVisionConfig( + name="TotalSegmentator_BoxSize_Task02_Coronal_Test", + dataset_name="TotalSegmentator", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="coronal", + split="test", + ), + MedVisionConfig( + name="TotalSegmentator_BoxSize_Task02_Axial_Train", + dataset_name="TotalSegmentator", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="train", + ), + MedVisionConfig( + name="TotalSegmentator_BoxSize_Task02_Axial_Test", + dataset_name="TotalSegmentator", + taskType="Box-Size", + taskID="02", + imageType="2D", + features_dict=features_dict_BoxSize, + imageSliceType="axial", + split="test", + ), + ] + + # Mapping from dataset name to package name + # NOTE: It is important to use the same dataset name as in: + # - MedVisionConfig() + # - DATASETS_NAME2PACKAGE + # NOTE: only use "_" in package names + DATASETS_NAME2PACKAGE = { + "ACDC": "ACDC", + "AMOS22": "AMOS22", + "AbdomenAtlas1.0Mini": "AbdomenAtlas__1_0__Mini", + "AbdomenCT-1K": "AbdomenCT_1K", + "BCV15": "BCV15", + "BraTS24": "BraTS24", + "CAMUS": "CAMUS", + "Ceph-Biometrics-400": "Ceph_Biometrics_400", + "CrossMoDA": "CrossMoDA", + "FLARE22": "FLARE22", + "FeTA24": "FeTA24", + "HNTSMRG24": "HNTSMRG24", + "ISLES24": "ISLES24", + "KiPA22": "KiPA22", + "KiTS23": "KiTS23", + "MSD": "MSD", + "OAIZIB-CM": "OAIZIB_CM", + "SKM-TEA": "SKM_TEA", + "ToothFairy2": "ToothFairy2", + "TopCoW24": "TopCoW24", + "TotalSegmentator": "TotalSegmentator", + "autoPET-III": "autoPET_III", + } + + def _info(self): + # Define dataset information including feature schema + dataset_description = f"You are using the configuration <{self.config.name}> of the <{self.config.dataset_name}> dataset." + return DatasetInfo( + description=_DESCRIPTION + "\n" + dataset_description, + features=Features(self.config.features_dict), + citation=_CITATION, + homepage=_HOME_PAGE, + license=_LICENSE, + ) + + def _split_generators(self, dl_manager): + # Set root directory for MedVision data + MedVision_data_dir = os.environ.get("MedVision_DATA_DIR") + os.makedirs(MedVision_data_dir, exist_ok=True) + + # Add a cache tracking mechanism + dataset_cache_file = os.path.join(MedVision_data_dir, ".downloaded_datasets.json") + + # Load existing download cache if it exists + downloaded_datasets = {} + if os.path.exists(dataset_cache_file): + try: + with open(dataset_cache_file, "r") as f: + downloaded_datasets = json.load(f) + except Exception as e: + logger.warning( + f"Error loading download cache file: {dataset_cache_file}\nError: {e}" + ) + downloaded_datasets = {} + + # Check if we should force downloads based on environment variables + force_download_data = ( + os.environ.get("MedVision_FORCE_DOWNLOAD_DATA", "False").lower() + == "true" + ) + force_install_code = ( + os.environ.get("MedVision_FORCE_INSTALL_CODE", "False").lower() + == "true" + ) + + # Track if we've already downloaded this dataset in this run + dataset_name = self.config.dataset_name + dataset_dir = os.path.join(MedVision_data_dir, "Datasets", dataset_name) + + # 1. Codebase download - should happen only once globally + if force_install_code or "medvision_ds" not in downloaded_datasets: + logger.info("Downloading the package from HuggingFace...") + snapshot_download( + repo_id="YongchengYAO/MedVision", + repo_type="dataset", + allow_patterns=["src/*"], + local_dir=MedVision_data_dir, + max_workers=self.config.num_proc, + ) + # Mark codebase as downloaded + downloaded_datasets["medvision_ds"] = True + with open(dataset_cache_file, "w") as f: + json.dump(downloaded_datasets, f) + else: + logger.info("Using cached package") + + # Make sure we point to the src directory itself + src_dir = os.path.join(MedVision_data_dir, "src") + logger.info(f" - Code located at: {src_dir}") + + # 2. Package installation - should happen only once globally + if force_install_code or "medvision_ds_installed" not in downloaded_datasets: + logger.info("Installing the package...") + current_dir = os.getcwd() + os.chdir(src_dir) + try: + logger.info(f"Installing : pip install {src_dir}") + subprocess.run( + ["pip", "install", "."], + check=True, + capture_output=True, + text=True, + ) + logger.info(" - The package installed successfully") + # Mark package as installed + downloaded_datasets["medvision_ds_installed"] = True + with open(dataset_cache_file, "w") as f: + json.dump(downloaded_datasets, f) + except subprocess.CalledProcessError as e: + logger.info(f" - Installation failed with error: {e.stderr}") + finally: + os.chdir(current_dir) + else: + logger.info("Using installed package") + + # 3. Dataset download - should happen only once per dataset + if force_download_data or f"dataset_{dataset_name}" not in downloaded_datasets: + logger.info(f"Downloading annotations to: {dataset_dir}") + + # 3.1 Download and extract the annotations + snapshot_download( + repo_id="YongchengYAO/MedVision", + repo_type="dataset", + allow_patterns=[f"Datasets/{dataset_name}.zip"], + local_dir=MedVision_data_dir, + max_workers=self.config.num_proc, + ) + zipfile_path = os.path.join( + MedVision_data_dir, "Datasets", f"{dataset_name}.zip" + ) + with zipfile.ZipFile(zipfile_path, "r") as zip_ref: + zip_ref.extractall(os.path.join(MedVision_data_dir, "Datasets")) + os.remove(zipfile_path) + logger.info(f" - Annotations downloaded to: {dataset_dir}") + + # 3.2 Download and process the image and/or mask files + logger.info("Downloading and processing image and/or mask files...") + # Look for download scripts in package_dir + dataset_package_name = self.DATASETS_NAME2PACKAGE[dataset_name] + package_medvision_ds = importlib.import_module("medvision_ds") + package_dir_medvision_datasets = os.path.join( + os.path.dirname(os.path.abspath(package_medvision_ds.__file__)), + "datasets", + ) + package_dir_medvision_dataset = os.path.join( + package_dir_medvision_datasets, dataset_package_name + ) + download_scripts = ["download_debug.py", "download.py", "download_fast.py", "download_raw.py"] + download_script_chosen = None + for script in download_scripts: + script_path = os.path.join(package_dir_medvision_dataset, script) + if os.path.exists(script_path): + download_script_chosen = script + break + else: + continue + if download_script_chosen is None: + raise ValueError( + f"Missing download script. There should be at least one of {download_scripts} in {package_dir_medvision_dataset}" + ) + # Use importlib to dynamically import the download module + current_dir = os.getcwd() + os.chdir(dataset_dir) + try: + logger.info(f" - Using download script: {download_script_chosen}") + script_path = os.path.join( + package_dir_medvision_dataset, download_script_chosen + ) + spec = importlib.util.spec_from_file_location( + "download_module", script_path + ) + download_module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(download_module) + # Execute the download function if it exists + if hasattr(download_module, "download_and_extract"): + # FIXME: max_workers not working properly in some download scripts + try: + download_module.download_and_extract(dataset_dir, dataset_name, max_workers=self.config.num_proc) + except: + download_module.download_and_extract(dataset_dir, dataset_name) + logger.info(f" - Download completed for {dataset_name}") + else: + logger.warning( + f" - download_and_extract function not found in {download_script_chosen}" + ) + except subprocess.CalledProcessError as e: + logger.info(f" - Download failed with error: {e.stderr}") + finally: + os.chdir(current_dir) + + # 3.3 Standardize the image and/or mask files: convert NIfTI files to RAS+ orientation + # NOTE: MUST NOT move the import to the front, as the "medvision_ds" package may not be installed yet at the beginning + from medvision_ds.utils.data_conversion import reorient_niigz_RASplus_batch_inplace + + reorient_niigz_RASplus_batch_inplace(dataset_dir, workers_limit=self.config.num_proc) + + # 3.4 Mark this dataset as downloaded + downloaded_datasets[f"dataset_{dataset_name}"] = True + with open(dataset_cache_file, "w") as f: + json.dump(downloaded_datasets, f) + else: + logger.info(f" - Using existing dataset at: {dataset_dir}") + + # 4. Get the benchmark planner file - this is config specific but should use existing files if possible + # NOTE: MUST NOT move the import to the front, as the "medvision_ds" package may not be installed yet at the beginning + from medvision_ds.utils.benchmark_planner import ( + MedVision_BenchmarkPlannerSegmentation, + MedVision_BenchmarkPlannerDetection, + MedVision_BenchmarkPlannerBiometry, + MedVision_BenchmarkPlannerBiometry_fromSeg, + ) + + if self.config.taskType in ["Biometrics-From-Landmarks", "Biometrics-From-Landmarks-Distance", "Biometrics-From-Landmarks-Angle"]: + get_bm_plan_file = MedVision_BenchmarkPlannerBiometry.get_bm_plan_file + elif self.config.taskType == "Mask-Size": + get_bm_plan_file = ( + MedVision_BenchmarkPlannerSegmentation.get_bm_plan_file + ) + elif self.config.taskType == "Box-Size": + get_bm_plan_file = MedVision_BenchmarkPlannerDetection.get_bm_plan_file + elif self.config.taskType == "Tumor-Lesion-Size": + get_bm_plan_file = ( + MedVision_BenchmarkPlannerBiometry_fromSeg.get_bm_plan_file + ) + else: + raise ValueError(f"Task type {self.config.taskType} not supported.") + + # NOTE: MUST NOT move the import to the front, as the "medvision_ds" package may not be installed yet at the beginning + from medvision_ds import __version__ + + bm_plan_file = get_bm_plan_file(dataset_dir, __version__) + + # Only generate the requested split + if self.config.split == "train": + return [ + SplitGenerator( + name=Split.TRAIN, + gen_kwargs={ + "dataset_dir": dataset_dir, + "bm_plan_file": bm_plan_file, + "split": "train", + "taskID": self.config.taskID, + "taskType": self.config.taskType, + "imageType": self.config.imageType, + "imageSliceType": self.config.imageSliceType, + }, + ) + ] + elif self.config.split == "test": + return [ + SplitGenerator( + name=Split.TEST, + gen_kwargs={ + "dataset_dir": dataset_dir, + "bm_plan_file": bm_plan_file, + "split": "test", + "taskID": self.config.taskID, + "taskType": self.config.taskType, + "imageType": self.config.imageType, + "imageSliceType": self.config.imageSliceType, + }, + ) + ] + # Generate both train and test splits (for lazy configuration where split is None) + elif self.config.split is None: + return [ + SplitGenerator( + name=Split.TRAIN, + gen_kwargs={ + "dataset_dir": dataset_dir, + "bm_plan_file": bm_plan_file, + "split": "train", + "taskID": self.config.taskID, + "taskType": self.config.taskType, + "imageType": self.config.imageType, + "imageSliceType": self.config.imageSliceType, + }, + ), + SplitGenerator( + name=Split.TEST, + gen_kwargs={ + "dataset_dir": dataset_dir, + "bm_plan_file": bm_plan_file, + "split": "test", + "taskID": self.config.taskID, + "taskType": self.config.taskType, + "imageType": self.config.imageType, + "imageSliceType": self.config.imageSliceType, + }, + ), + ] + else: + raise ValueError(f"Invalid split: {self.config.split}") + + def _generate_examples( + self, + dataset_dir, + bm_plan_file, + split, + taskID, + taskType, + imageType, + imageSliceType, + ): + # NOTE: MUST NOT move the import to the front, as the "medvision_ds" package may not be installed yet at the beginning + from medvision_ds.utils.benchmark_planner import ( + MedVision_BenchmarkPlannerSegmentation, + MedVision_BenchmarkPlannerDetection, + MedVision_BenchmarkPlannerBiometry, + MedVision_BenchmarkPlannerBiometry_fromSeg, + ) + + # Load benchmark plan file + if bm_plan_file.endswith(".gz"): + with gzip.open(bm_plan_file, "rt") as f: + benchmark_plan = json.load(f) + else: + with open(bm_plan_file, "r") as f: + benchmark_plan = json.load(f) + + # Get annotation data: a list of dictionaries + if split == "train": + biometricData = benchmark_plan["tasks"][int(taskID) - 1]["train_cases"] + elif split == "test": + biometricData = benchmark_plan["tasks"][int(taskID) - 1]["test_cases"] + else: + raise ValueError(f"Unknown split: {split}") + + # Task type: Mask-Size + if taskType == "Mask-Size": + flatten_slice_profiles = ( + MedVision_BenchmarkPlannerSegmentation.flatten_slice_profiles_2d + ) + if imageSliceType.lower() == "sagittal": + slice_dim = 0 + elif imageSliceType.lower() == "coronal": + slice_dim = 1 + elif imageSliceType.lower() == "axial": + slice_dim = 2 + slice_profile_flattened = flatten_slice_profiles(biometricData, slice_dim) + for idx, case in enumerate(slice_profile_flattened): + # Skip cases with a mask size smaller than 200 pixels + if case["pixel_count"] < 200: + continue + else: + yield idx, { + "taskID": taskID, + "taskType": taskType, + "image_file": os.path.join(dataset_dir, case["image_file"]), + "mask_file": os.path.join(dataset_dir, case["mask_file"]), + "slice_dim": case["slice_dim"], + "slice_idx": case["slice_idx"], + "label": case["label"], + "image_size_2d": case["image_size_2d"], + "pixel_size": case["pixel_size"], + "image_size_3d": case["image_size_3d"], + "voxel_size": case["voxel_size"], + "pixel_count": case["pixel_count"], + "ROI_area": case["ROI_area"], + } + + # Task type: Box-Size + if taskType == "Box-Size": + if imageType.lower() == "2d": + flatten_slice_profiles = ( + MedVision_BenchmarkPlannerDetection.flatten_slice_profiles_2d + ) + if imageSliceType.lower() == "sagittal": + slice_dim = 0 + elif imageSliceType.lower() == "coronal": + slice_dim = 1 + elif imageSliceType.lower() == "axial": + slice_dim = 2 + slice_profile_flattened = flatten_slice_profiles( + biometricData, slice_dim + ) + for idx, case in enumerate(slice_profile_flattened): + # Skip cases with multiple bounding boxes in the same slice + if len(case["bounding_boxes"]) > 1: + continue + # Skip cases with a bounding box size smaller than 10 pixels in any dimension + elif ( + case["bounding_boxes"][0]["dimensions"][0] < 10 + or case["bounding_boxes"][0]["dimensions"][1] < 10 + ): + continue + else: + yield idx, { + "taskID": taskID, + "taskType": taskType, + "image_file": os.path.join(dataset_dir, case["image_file"]), + "mask_file": os.path.join(dataset_dir, case["mask_file"]), + "slice_dim": case["slice_dim"], + "slice_idx": case["slice_idx"], + "label": case["label"], + "image_size_2d": case["image_size_2d"], + "pixel_size": case["pixel_size"], + "image_size_3d": case["image_size_3d"], + "voxel_size": case["voxel_size"], + "bounding_boxes": case["bounding_boxes"], + } + + # Task type: Biometrics-From-Landmarks + if taskType == "Biometrics-From-Landmarks": + if imageType.lower() == "2d": + flatten_slice_profiles = ( + MedVision_BenchmarkPlannerBiometry.flatten_slice_profiles_2d + ) + if imageSliceType.lower() == "sagittal": + slice_dim = 0 + elif imageSliceType.lower() == "coronal": + slice_dim = 1 + elif imageSliceType.lower() == "axial": + slice_dim = 2 + slice_profile_flattened = flatten_slice_profiles( + biometricData, slice_dim + ) + for idx, case in enumerate(slice_profile_flattened): + yield idx, { + "taskID": taskID, + "taskType": taskType, + "image_file": os.path.join(dataset_dir, case["image_file"]), + "landmark_file": os.path.join( + dataset_dir, case["landmark_file"] + ), + "slice_dim": case["slice_dim"], + "slice_idx": case["slice_idx"], + "image_size_2d": case["image_size_2d"], + "pixel_size": case["pixel_size"], + "image_size_3d": case["image_size_3d"], + "voxel_size": case["voxel_size"], + "biometric_profile": case["biometric_profile"], + } + + # Task type: Biometrics-From-Landmarks-Distance + if taskType == "Biometrics-From-Landmarks-Distance": + if imageType.lower() == "2d": + flatten_slice_profiles = ( + MedVision_BenchmarkPlannerBiometry.flatten_slice_profiles_2d + ) + if imageSliceType.lower() == "sagittal": + slice_dim = 0 + elif imageSliceType.lower() == "coronal": + slice_dim = 1 + elif imageSliceType.lower() == "axial": + slice_dim = 2 + slice_profile_flattened = flatten_slice_profiles( + biometricData, slice_dim + ) + for idx, case in enumerate(slice_profile_flattened): + if case["biometric_profile"]["metric_type"] == "distance": + yield idx, { + "taskID": taskID, + "taskType": taskType, + "image_file": os.path.join(dataset_dir, case["image_file"]), + "landmark_file": os.path.join( + dataset_dir, case["landmark_file"] + ), + "slice_dim": case["slice_dim"], + "slice_idx": case["slice_idx"], + "image_size_2d": case["image_size_2d"], + "pixel_size": case["pixel_size"], + "image_size_3d": case["image_size_3d"], + "voxel_size": case["voxel_size"], + "biometric_profile": case["biometric_profile"], + } + + # Task type: Biometrics-From-Landmarks-Angle + if taskType == "Biometrics-From-Landmarks-Angle": + if imageType.lower() == "2d": + flatten_slice_profiles = ( + MedVision_BenchmarkPlannerBiometry.flatten_slice_profiles_2d + ) + if imageSliceType.lower() == "sagittal": + slice_dim = 0 + elif imageSliceType.lower() == "coronal": + slice_dim = 1 + elif imageSliceType.lower() == "axial": + slice_dim = 2 + slice_profile_flattened = flatten_slice_profiles( + biometricData, slice_dim + ) + for idx, case in enumerate(slice_profile_flattened): + if case["biometric_profile"]["metric_type"] == "angle": + yield idx, { + "taskID": taskID, + "taskType": taskType, + "image_file": os.path.join(dataset_dir, case["image_file"]), + "landmark_file": os.path.join( + dataset_dir, case["landmark_file"] + ), + "slice_dim": case["slice_dim"], + "slice_idx": case["slice_idx"], + "image_size_2d": case["image_size_2d"], + "pixel_size": case["pixel_size"], + "image_size_3d": case["image_size_3d"], + "voxel_size": case["voxel_size"], + "biometric_profile": case["biometric_profile"], + } + + # Task type: Tumor-Lesion-Size + if taskType == "Tumor-Lesion-Size": + if imageType.lower() == "2d": + # Get the target label for the task + target_label = benchmark_plan["tasks"][int(taskID) - 1]["target_label"] + + flatten_slice_profiles = ( + MedVision_BenchmarkPlannerBiometry_fromSeg.flatten_slice_profiles_2d + ) + if imageSliceType.lower() == "sagittal": + slice_dim = 0 + elif imageSliceType.lower() == "coronal": + slice_dim = 1 + elif imageSliceType.lower() == "axial": + slice_dim = 2 + slice_profile_flattened = flatten_slice_profiles( + biometricData, slice_dim + ) + for idx, case in enumerate(slice_profile_flattened): + # Skip cases with multiple fitted ellipses in the same slice + if len(case["biometric_profile"]) > 1: + continue + else: + yield idx, { + "taskID": taskID, + "taskType": taskType, + "image_file": os.path.join(dataset_dir, case["image_file"]), + "mask_file": os.path.join(dataset_dir, case["mask_file"]), + "landmark_file": os.path.join( + dataset_dir, case["landmark_file"] + ), + "slice_dim": case["slice_dim"], + "slice_idx": case["slice_idx"], + "label": target_label, + "image_size_2d": case["image_size_2d"], + "pixel_size": case["pixel_size"], + "image_size_3d": case["image_size_3d"], + "voxel_size": case["voxel_size"], + "biometric_profile": case["biometric_profile"], + } diff --git a/README.md b/README.md index 1f48c32e9df46945f0ac7142bfda001a1beb4747..251880e1d1e4c1d0429de1f6700588fb1788cd5d 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,42 @@ ---- -license: cc-by-nc-4.0 ---- +# About +**MedVision**, a large-scale, multi-anatomy, multi-modality dataset for quantitative medical image analysis. + +# News +- [Oct 8, 2025] 🚀 Release **MedVision** dataset v1.0.0 + +# Requirement +📝 Note: `trust_remote_code` is no longer supported in datasets>=4.0.0, install `dataset` with `pip install datasets==3.6.0` + +# Use +```python +import os +from datasets import load_dataset, config + +# Set data folder +os.environ["MedVision_DATA_DIR"] = + +# Pick a dataset config name and split +config = +split_name = "test", # use "test" for testing set config; use "train" for training set config + +# Get dataset +ds = load_dataset( + "YongchengYAO/MedVision", + name=config, + trust_remote_code=True, + split=split_name, + ) +``` +📝 List of config names in `info/` + +# Environment Variables +```bash +# Set where data will be saved, requires ~1T for the complete dataset +export MedVision_DATA_DIR= + +# Force download and process raw images, default to "False" +export MedVision_FORCE_DOWNLOAD_DATA="False" + +# Force install dataset codebase, default to "False" +export MedVision_FORCE_INSTALL_CODE="False" +``` \ No newline at end of file diff --git a/info/ConfigurationsList_All.csv b/info/ConfigurationsList_All.csv new file mode 100644 index 0000000000000000000000000000000000000000..58e7571f88d4da3bbad189a230ecc35f26543115 --- /dev/null +++ b/info/ConfigurationsList_All.csv @@ -0,0 +1,820 @@ +AbdomenAtlas1.0Mini_MaskSize_Task01_Sagittal_Train +AbdomenAtlas1.0Mini_MaskSize_Task01_Sagittal_Test +AbdomenAtlas1.0Mini_MaskSize_Task01_Coronal_Train +AbdomenAtlas1.0Mini_MaskSize_Task01_Coronal_Test +AbdomenAtlas1.0Mini_MaskSize_Task01_Axial_Train +AbdomenAtlas1.0Mini_MaskSize_Task01_Axial_Test +AbdomenAtlas1.0Mini_BoxSize_Task01_Sagittal_Train +AbdomenAtlas1.0Mini_BoxSize_Task01_Sagittal_Test +AbdomenAtlas1.0Mini_BoxSize_Task01_Coronal_Train +AbdomenAtlas1.0Mini_BoxSize_Task01_Coronal_Test +AbdomenAtlas1.0Mini_BoxSize_Task01_Axial_Train +AbdomenAtlas1.0Mini_BoxSize_Task01_Axial_Test +AbdomenCT-1K_MaskSize_Task01_Sagittal_Train +AbdomenCT-1K_MaskSize_Task01_Sagittal_Test +AbdomenCT-1K_MaskSize_Task01_Coronal_Train +AbdomenCT-1K_MaskSize_Task01_Coronal_Test +AbdomenCT-1K_MaskSize_Task01_Axial_Train +AbdomenCT-1K_MaskSize_Task01_Axial_Test +AbdomenCT-1K_BoxSize_Task01_Sagittal_Train +AbdomenCT-1K_BoxSize_Task01_Sagittal_Test +AbdomenCT-1K_BoxSize_Task01_Coronal_Train +AbdomenCT-1K_BoxSize_Task01_Coronal_Test +AbdomenCT-1K_BoxSize_Task01_Axial_Train +AbdomenCT-1K_BoxSize_Task01_Axial_Test +ACDC_MaskSize_Task01_Sagittal_Train +ACDC_MaskSize_Task01_Sagittal_Test +ACDC_MaskSize_Task01_Coronal_Train +ACDC_MaskSize_Task01_Coronal_Test +ACDC_MaskSize_Task01_Axial_Train +ACDC_MaskSize_Task01_Axial_Test +ACDC_BoxSize_Task01_Sagittal_Train +ACDC_BoxSize_Task01_Sagittal_Test +ACDC_BoxSize_Task01_Coronal_Train +ACDC_BoxSize_Task01_Coronal_Test +ACDC_BoxSize_Task01_Axial_Train +ACDC_BoxSize_Task01_Axial_Test +AMOS22_MaskSize_Task01_Sagittal_Train +AMOS22_MaskSize_Task01_Sagittal_Test +AMOS22_MaskSize_Task01_Coronal_Train +AMOS22_MaskSize_Task01_Coronal_Test +AMOS22_MaskSize_Task01_Axial_Train +AMOS22_MaskSize_Task01_Axial_Test +AMOS22_MaskSize_Task02_Sagittal_Train +AMOS22_MaskSize_Task02_Sagittal_Test +AMOS22_MaskSize_Task02_Coronal_Train +AMOS22_MaskSize_Task02_Coronal_Test +AMOS22_MaskSize_Task02_Axial_Train +AMOS22_MaskSize_Task02_Axial_Test +AMOS22_BoxSize_Task01_Sagittal_Train +AMOS22_BoxSize_Task01_Sagittal_Test +AMOS22_BoxSize_Task01_Coronal_Train +AMOS22_BoxSize_Task01_Coronal_Test +AMOS22_BoxSize_Task01_Axial_Train +AMOS22_BoxSize_Task01_Axial_Test +AMOS22_BoxSize_Task02_Sagittal_Train +AMOS22_BoxSize_Task02_Sagittal_Test +AMOS22_BoxSize_Task02_Coronal_Train +AMOS22_BoxSize_Task02_Coronal_Test +AMOS22_BoxSize_Task02_Axial_Train +AMOS22_BoxSize_Task02_Axial_Test +autoPET-III_MaskSize_Task01_Sagittal_Train +autoPET-III_MaskSize_Task01_Sagittal_Test +autoPET-III_MaskSize_Task01_Coronal_Train +autoPET-III_MaskSize_Task01_Coronal_Test +autoPET-III_MaskSize_Task01_Axial_Train +autoPET-III_MaskSize_Task01_Axial_Test +autoPET-III_MaskSize_Task02_Sagittal_Train +autoPET-III_MaskSize_Task02_Sagittal_Test +autoPET-III_MaskSize_Task02_Coronal_Train +autoPET-III_MaskSize_Task02_Coronal_Test +autoPET-III_MaskSize_Task02_Axial_Train +autoPET-III_MaskSize_Task02_Axial_Test +autoPET-III_BoxSize_Task01_Sagittal_Train +autoPET-III_BoxSize_Task01_Sagittal_Test +autoPET-III_BoxSize_Task01_Coronal_Train +autoPET-III_BoxSize_Task01_Coronal_Test +autoPET-III_BoxSize_Task01_Axial_Train +autoPET-III_BoxSize_Task01_Axial_Test +autoPET-III_BoxSize_Task02_Sagittal_Train +autoPET-III_BoxSize_Task02_Sagittal_Test +autoPET-III_BoxSize_Task02_Coronal_Train +autoPET-III_BoxSize_Task02_Coronal_Test +autoPET-III_BoxSize_Task02_Axial_Train +autoPET-III_BoxSize_Task02_Axial_Test +autoPET-III_TumorLesionSize_Task01_Sagittal_Train +autoPET-III_TumorLesionSize_Task01_Sagittal_Test +autoPET-III_TumorLesionSize_Task01_Coronal_Train +autoPET-III_TumorLesionSize_Task01_Coronal_Test +autoPET-III_TumorLesionSize_Task01_Axial_Train +autoPET-III_TumorLesionSize_Task01_Axial_Test +BCV15_MaskSize_Task01_Sagittal_Train +BCV15_MaskSize_Task01_Sagittal_Test +BCV15_MaskSize_Task01_Coronal_Train +BCV15_MaskSize_Task01_Coronal_Test +BCV15_MaskSize_Task01_Axial_Train +BCV15_MaskSize_Task01_Axial_Test +BCV15_MaskSize_Task02_Sagittal_Train +BCV15_MaskSize_Task02_Sagittal_Test +BCV15_MaskSize_Task02_Coronal_Train +BCV15_MaskSize_Task02_Coronal_Test +BCV15_MaskSize_Task02_Axial_Train +BCV15_MaskSize_Task02_Axial_Test +BCV15_BoxSize_Task01_Sagittal_Train +BCV15_BoxSize_Task01_Sagittal_Test +BCV15_BoxSize_Task01_Coronal_Train +BCV15_BoxSize_Task01_Coronal_Test +BCV15_BoxSize_Task01_Axial_Train +BCV15_BoxSize_Task01_Axial_Test +BCV15_BoxSize_Task02_Sagittal_Train +BCV15_BoxSize_Task02_Sagittal_Test +BCV15_BoxSize_Task02_Coronal_Train +BCV15_BoxSize_Task02_Coronal_Test +BCV15_BoxSize_Task02_Axial_Train +BCV15_BoxSize_Task02_Axial_Test +BraTS24_MaskSize_Task01_Sagittal_Train +BraTS24_MaskSize_Task01_Sagittal_Test +BraTS24_MaskSize_Task01_Coronal_Train +BraTS24_MaskSize_Task01_Coronal_Test +BraTS24_MaskSize_Task01_Axial_Train +BraTS24_MaskSize_Task01_Axial_Test +BraTS24_MaskSize_Task02_Sagittal_Train +BraTS24_MaskSize_Task02_Sagittal_Test +BraTS24_MaskSize_Task02_Coronal_Train +BraTS24_MaskSize_Task02_Coronal_Test +BraTS24_MaskSize_Task02_Axial_Train +BraTS24_MaskSize_Task02_Axial_Test +BraTS24_MaskSize_Task03_Sagittal_Train +BraTS24_MaskSize_Task03_Sagittal_Test +BraTS24_MaskSize_Task03_Coronal_Train +BraTS24_MaskSize_Task03_Coronal_Test +BraTS24_MaskSize_Task03_Axial_Train +BraTS24_MaskSize_Task03_Axial_Test +BraTS24_MaskSize_Task04_Sagittal_Train +BraTS24_MaskSize_Task04_Sagittal_Test +BraTS24_MaskSize_Task04_Coronal_Train +BraTS24_MaskSize_Task04_Coronal_Test +BraTS24_MaskSize_Task04_Axial_Train +BraTS24_MaskSize_Task04_Axial_Test +BraTS24_MaskSize_Task05_Sagittal_Train +BraTS24_MaskSize_Task05_Sagittal_Test +BraTS24_MaskSize_Task05_Coronal_Train +BraTS24_MaskSize_Task05_Coronal_Test +BraTS24_MaskSize_Task05_Axial_Train +BraTS24_MaskSize_Task05_Axial_Test +BraTS24_MaskSize_Task06_Sagittal_Train +BraTS24_MaskSize_Task06_Sagittal_Test +BraTS24_MaskSize_Task06_Coronal_Train +BraTS24_MaskSize_Task06_Coronal_Test +BraTS24_MaskSize_Task06_Axial_Train +BraTS24_MaskSize_Task06_Axial_Test +BraTS24_MaskSize_Task07_Sagittal_Train +BraTS24_MaskSize_Task07_Sagittal_Test +BraTS24_MaskSize_Task07_Coronal_Train +BraTS24_MaskSize_Task07_Coronal_Test +BraTS24_MaskSize_Task07_Axial_Train +BraTS24_MaskSize_Task07_Axial_Test +BraTS24_MaskSize_Task08_Sagittal_Train +BraTS24_MaskSize_Task08_Sagittal_Test +BraTS24_MaskSize_Task08_Coronal_Train +BraTS24_MaskSize_Task08_Coronal_Test +BraTS24_MaskSize_Task08_Axial_Train +BraTS24_MaskSize_Task08_Axial_Test +BraTS24_MaskSize_Task09_Sagittal_Train +BraTS24_MaskSize_Task09_Sagittal_Test +BraTS24_MaskSize_Task09_Coronal_Train +BraTS24_MaskSize_Task09_Coronal_Test +BraTS24_MaskSize_Task09_Axial_Train +BraTS24_MaskSize_Task09_Axial_Test +BraTS24_MaskSize_Task10_Sagittal_Train +BraTS24_MaskSize_Task10_Sagittal_Test +BraTS24_MaskSize_Task10_Coronal_Train +BraTS24_MaskSize_Task10_Coronal_Test +BraTS24_MaskSize_Task10_Axial_Train +BraTS24_MaskSize_Task10_Axial_Test +BraTS24_MaskSize_Task11_Sagittal_Train +BraTS24_MaskSize_Task11_Sagittal_Test +BraTS24_MaskSize_Task11_Coronal_Train +BraTS24_MaskSize_Task11_Coronal_Test +BraTS24_MaskSize_Task11_Axial_Train +BraTS24_MaskSize_Task11_Axial_Test +BraTS24_MaskSize_Task12_Sagittal_Train +BraTS24_MaskSize_Task12_Sagittal_Test +BraTS24_MaskSize_Task12_Coronal_Train +BraTS24_MaskSize_Task12_Coronal_Test +BraTS24_MaskSize_Task12_Axial_Train +BraTS24_MaskSize_Task12_Axial_Test +BraTS24_MaskSize_Task13_Sagittal_Train +BraTS24_MaskSize_Task13_Sagittal_Test +BraTS24_MaskSize_Task13_Coronal_Train 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a/info/ConfigurationsList_Test.csv b/info/ConfigurationsList_Test.csv new file mode 100644 index 0000000000000000000000000000000000000000..24e77b4e1e4f4b461fbd54f0bcbb356110391ec2 --- /dev/null +++ b/info/ConfigurationsList_Test.csv @@ -0,0 +1,410 @@ +AbdomenAtlas1.0Mini_MaskSize_Task01_Sagittal_Test +AbdomenAtlas1.0Mini_MaskSize_Task01_Coronal_Test +AbdomenAtlas1.0Mini_MaskSize_Task01_Axial_Test +AbdomenAtlas1.0Mini_BoxSize_Task01_Sagittal_Test +AbdomenAtlas1.0Mini_BoxSize_Task01_Coronal_Test +AbdomenAtlas1.0Mini_BoxSize_Task01_Axial_Test +AbdomenCT-1K_MaskSize_Task01_Sagittal_Test +AbdomenCT-1K_MaskSize_Task01_Coronal_Test +AbdomenCT-1K_MaskSize_Task01_Axial_Test +AbdomenCT-1K_BoxSize_Task01_Sagittal_Test +AbdomenCT-1K_BoxSize_Task01_Coronal_Test +AbdomenCT-1K_BoxSize_Task01_Axial_Test +ACDC_MaskSize_Task01_Sagittal_Test +ACDC_MaskSize_Task01_Coronal_Test +ACDC_MaskSize_Task01_Axial_Test +ACDC_BoxSize_Task01_Sagittal_Test +ACDC_BoxSize_Task01_Coronal_Test 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+MSD_BoxSize_Task03_Coronal_Train +MSD_BoxSize_Task03_Axial_Train +MSD_BoxSize_Task04_Sagittal_Train +MSD_BoxSize_Task04_Coronal_Train +MSD_BoxSize_Task04_Axial_Train +MSD_BoxSize_Task05_Sagittal_Train +MSD_BoxSize_Task05_Coronal_Train +MSD_BoxSize_Task05_Axial_Train +MSD_BoxSize_Task06_Sagittal_Train +MSD_BoxSize_Task06_Coronal_Train +MSD_BoxSize_Task06_Axial_Train +MSD_BoxSize_Task07_Sagittal_Train +MSD_BoxSize_Task07_Coronal_Train +MSD_BoxSize_Task07_Axial_Train +MSD_BoxSize_Task08_Sagittal_Train +MSD_BoxSize_Task08_Coronal_Train +MSD_BoxSize_Task08_Axial_Train +MSD_BoxSize_Task09_Sagittal_Train +MSD_BoxSize_Task09_Coronal_Train +MSD_BoxSize_Task09_Axial_Train +MSD_BoxSize_Task10_Sagittal_Train +MSD_BoxSize_Task10_Coronal_Train +MSD_BoxSize_Task10_Axial_Train +MSD_BoxSize_Task11_Sagittal_Train +MSD_BoxSize_Task11_Coronal_Train +MSD_BoxSize_Task11_Axial_Train +MSD_BoxSize_Task12_Sagittal_Train +MSD_BoxSize_Task12_Coronal_Train +MSD_BoxSize_Task12_Axial_Train +MSD_BoxSize_Task13_Sagittal_Train +MSD_BoxSize_Task13_Coronal_Train +MSD_BoxSize_Task13_Axial_Train +MSD_BoxSize_Task14_Sagittal_Train +MSD_BoxSize_Task14_Coronal_Train +MSD_BoxSize_Task14_Axial_Train +MSD_TumorLesionSize_Task01_Sagittal_Train +MSD_TumorLesionSize_Task01_Coronal_Train +MSD_TumorLesionSize_Task01_Axial_Train +MSD_TumorLesionSize_Task02_Sagittal_Train +MSD_TumorLesionSize_Task02_Coronal_Train +MSD_TumorLesionSize_Task02_Axial_Train +MSD_TumorLesionSize_Task03_Sagittal_Train +MSD_TumorLesionSize_Task03_Coronal_Train +MSD_TumorLesionSize_Task03_Axial_Train +MSD_TumorLesionSize_Task04_Sagittal_Train +MSD_TumorLesionSize_Task04_Coronal_Train +MSD_TumorLesionSize_Task04_Axial_Train +MSD_TumorLesionSize_Task05_Sagittal_Train +MSD_TumorLesionSize_Task05_Coronal_Train +MSD_TumorLesionSize_Task05_Axial_Train +MSD_TumorLesionSize_Task06_Sagittal_Train +MSD_TumorLesionSize_Task06_Coronal_Train +MSD_TumorLesionSize_Task06_Axial_Train +MSD_TumorLesionSize_Task07_Sagittal_Train +MSD_TumorLesionSize_Task07_Coronal_Train +MSD_TumorLesionSize_Task07_Axial_Train +MSD_TumorLesionSize_Task08_Sagittal_Train +MSD_TumorLesionSize_Task08_Coronal_Train +MSD_TumorLesionSize_Task08_Axial_Train +OAIZIB-CM_MaskSize_Task01_Sagittal_Train +OAIZIB-CM_MaskSize_Task01_Coronal_Train +OAIZIB-CM_MaskSize_Task01_Axial_Train +OAIZIB-CM_BoxSize_Task01_Sagittal_Train +OAIZIB-CM_BoxSize_Task01_Coronal_Train +OAIZIB-CM_BoxSize_Task01_Axial_Train +SKM-TEA_MaskSize_Task01_Sagittal_Train +SKM-TEA_MaskSize_Task01_Coronal_Train +SKM-TEA_MaskSize_Task01_Axial_Train +SKM-TEA_MaskSize_Task02_Sagittal_Train +SKM-TEA_MaskSize_Task02_Coronal_Train +SKM-TEA_MaskSize_Task02_Axial_Train +SKM-TEA_BoxSize_Task01_Sagittal_Train +SKM-TEA_BoxSize_Task01_Coronal_Train +SKM-TEA_BoxSize_Task01_Axial_Train +SKM-TEA_BoxSize_Task02_Sagittal_Train +SKM-TEA_BoxSize_Task02_Coronal_Train +SKM-TEA_BoxSize_Task02_Axial_Train +ToothFairy2_MaskSize_Task01_Sagittal_Train +ToothFairy2_MaskSize_Task01_Coronal_Train +ToothFairy2_MaskSize_Task01_Axial_Train +ToothFairy2_BoxSize_Task01_Sagittal_Train +ToothFairy2_BoxSize_Task01_Coronal_Train +ToothFairy2_BoxSize_Task01_Axial_Train +TopCoW24_MaskSize_Task01_Sagittal_Train +TopCoW24_MaskSize_Task01_Coronal_Train +TopCoW24_MaskSize_Task01_Axial_Train +TopCoW24_MaskSize_Task02_Sagittal_Train +TopCoW24_MaskSize_Task02_Coronal_Train +TopCoW24_MaskSize_Task02_Axial_Train +TopCoW24_BoxSize_Task01_Sagittal_Train +TopCoW24_BoxSize_Task01_Coronal_Train +TopCoW24_BoxSize_Task01_Axial_Train +TopCoW24_BoxSize_Task02_Sagittal_Train +TopCoW24_BoxSize_Task02_Coronal_Train +TopCoW24_BoxSize_Task02_Axial_Train +TotalSegmentator_MaskSize_Task01_Sagittal_Train +TotalSegmentator_MaskSize_Task01_Coronal_Train +TotalSegmentator_MaskSize_Task01_Axial_Train +TotalSegmentator_MaskSize_Task02_Sagittal_Train +TotalSegmentator_MaskSize_Task02_Coronal_Train +TotalSegmentator_MaskSize_Task02_Axial_Train +TotalSegmentator_BoxSize_Task01_Sagittal_Train +TotalSegmentator_BoxSize_Task01_Coronal_Train +TotalSegmentator_BoxSize_Task01_Axial_Train +TotalSegmentator_BoxSize_Task02_Sagittal_Train +TotalSegmentator_BoxSize_Task02_Coronal_Train +TotalSegmentator_BoxSize_Task02_Axial_Train diff --git a/src/medvision_ds/__init__.py b/src/medvision_ds/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..efc8b0c64433873eb61b88e4f6bfeb22ac804854 --- /dev/null +++ b/src/medvision_ds/__init__.py @@ -0,0 +1,6 @@ +"""BiometricVQA package.""" +from . import utils +from . import datasets +from .__version__ import __version__ + +__all__ = ["utils", "datasets", "__version__"] diff --git a/src/medvision_ds/__version__.py b/src/medvision_ds/__version__.py new file mode 100644 index 0000000000000000000000000000000000000000..5becc17c04a9e3ad1c2a15f53252b7bb5a7517e7 --- /dev/null +++ b/src/medvision_ds/__version__.py @@ -0,0 +1 @@ +__version__ = "1.0.0" diff --git a/src/medvision_ds/datasets/ACDC/__init__.py b/src/medvision_ds/datasets/ACDC/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/ACDC/download_fast.py b/src/medvision_ds/datasets/ACDC/download_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..f13533defd44813236a3176dba5fdd48dbcf7343 --- /dev/null +++ b/src/medvision_ds/datasets/ACDC/download_fast.py @@ -0,0 +1,102 @@ +import os +import shutil +import argparse +import zipfile +from huggingface_hub import hf_hub_download +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: ACDC +# Data: https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download and extract dataset + for file in ["Images.zip", "Masks.zip"]: + # Download and extract dataset + hf_hub_download( + repo_id="YongchengYAO/ACDC", + filename=file, + repo_type="dataset", + revision="660f25484d2bd11d0416acdf332b97fd97f9c453", # commit hash on 2025-02-20 + local_dir=".", + ) + print(f"Extracting {file}... This may take some time.") + with zipfile.ZipFile(file, "r") as zip_ref: + zip_ref.extractall() + os.remove(file) + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/ACDC/download_raw.py b/src/medvision_ds/datasets/ACDC/download_raw.py new file mode 100644 index 0000000000000000000000000000000000000000..971c38d69b92bbc79f735f2ba1181f625f1f01eb --- /dev/null +++ b/src/medvision_ds/datasets/ACDC/download_raw.py @@ -0,0 +1,122 @@ +import os +import shutil +import argparse +import zipfile +import urllib.request +from medvision_ds.utils.preprocess_utils import process_dataset, move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: ACDC +# Data: https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download dataset using pure Python + print("Downloading ACDC.zip...") + urllib.request.urlretrieve( + "https://humanheart-project.creatis.insa-lyon.fr/database/api/v1/collection/637218c173e9f0047faa00fb/download", + "ACDC.zip", + ) + + # Extract and cleanup + print("Extracting ACDC.zip...") + with zipfile.ZipFile("ACDC.zip", "r") as zip_ref: + zip_ref.extractall() + os.remove("ACDC.zip") + + # Reorganize directory structure + shutil.move(os.path.join("ACDC", "database"), ".") + shutil.rmtree("ACDC") + + # Remove documentation and consolidate data + shutil.move(os.path.join("database", "testing"), ".") + shutil.move(os.path.join("database", "training"), ".") + shutil.rmtree("database") + + # Move testing contents to training + for item in os.listdir("testing"): + if item.startswith("patient"): + shutil.move(os.path.join("testing", item), "training") + shutil.rmtree("testing") + + # Create directories + os.makedirs("Images", exist_ok=True) + os.makedirs("Masks", exist_ok=True) + + # Process and organize files + process_dataset(["training"], "*_gt.nii.gz", "_gt.nii.gz", replace=False) + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/ACDC/preprocess_detection.py b/src/medvision_ds/datasets/ACDC/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..a3c1c3c540c6052afc3b40877e0405c4bf25b543 --- /dev/null +++ b/src/medvision_ds/datasets/ACDC/preprocess_detection.py @@ -0,0 +1,128 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "ACDC", + "dataset_website": " https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html", + "dataset_data": [ + "https://humanheart-project.creatis.insa-lyon.fr/database/#collection/637218c173e9f0047faa00fb", + ], + "license": ["CC BY-NC-SA 4.0"], + "paper": ["https://doi.org/10.1109/TMI.2018.2837502"], +} + +labels_map = { + "1": "right ventricular cavity", + "2": "myocardium", + "3": "left ventricular cavity", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "cardiac magnetic resonance imaging (MRI) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": "_gt.nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/ACDC/preprocess_segmentation.py b/src/medvision_ds/datasets/ACDC/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..458e13aaff18927c5fac9c34f3f322ace5db0c82 --- /dev/null +++ b/src/medvision_ds/datasets/ACDC/preprocess_segmentation.py @@ -0,0 +1,128 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "ACDC", + "dataset_website": " https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html", + "dataset_data": [ + "https://humanheart-project.creatis.insa-lyon.fr/database/#collection/637218c173e9f0047faa00fb", + ], + "license": ["CC BY-NC-SA 4.0"], + "paper": ["https://doi.org/10.1109/TMI.2018.2837502"], +} + +labels_map = { + "1": "right ventricular cavity", + "2": "myocardium", + "3": "left ventricular cavity", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "cardiac magnetic resonance imaging (MRI) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": "_gt.nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/AMOS22/__init__.py b/src/medvision_ds/datasets/AMOS22/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/AMOS22/download.py b/src/medvision_ds/datasets/AMOS22/download.py new file mode 100644 index 0000000000000000000000000000000000000000..ac378d6933198b26a4f6001414a55a698263a1fc --- /dev/null +++ b/src/medvision_ds/datasets/AMOS22/download.py @@ -0,0 +1,130 @@ +import os +import shutil +import argparse +import glob +import zipfile +import urllib.request +from medvision_ds.utils.preprocess_utils import move_folder + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: AMOS22 +# Challenge: https://amos22.grand-challenge.org +# Data: https://zenodo.org/records/7262581 +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download dataset + print("Downloading amos22.zip...") + urllib.request.urlretrieve( + "https://zenodo.org/records/7155725/files/amos22.zip?download=1", "amos22.zip" + ) + + # Extract zip + print("Extracting amos22.zip...") + with zipfile.ZipFile("amos22.zip", "r") as zip_ref: + zip_ref.extractall() + + # Move contents + for item in os.listdir("amos22"): + shutil.move(os.path.join("amos22", item), ".") + + # Create directories + for modality in ["CT", "MRI"]: + for subdir in ["Images", "Masks"]: + os.makedirs(os.path.join(f"AMOS22-{modality}", subdir), exist_ok=True) + + # Move image files + for folder in ["imagesTr", "imagesVa"]: + if os.path.exists(folder): + for f in glob.glob(os.path.join(folder, "amos_????.nii.gz")): + # Extract the number from filename + num = int(os.path.basename(f)[5:9]) + if num < 507: + shutil.move(f, os.path.join("AMOS22-CT", "Images")) + else: + shutil.move(f, os.path.join("AMOS22-MRI", "Images")) + + # Move mask files + for folder in ["labelsTr", "labelsVa"]: + if os.path.exists(folder): + for f in glob.glob(os.path.join(folder, "amos_????.nii.gz")): + # Extract the number from filename + num = int(os.path.basename(f)[5:9]) + if num < 507: + shutil.move(f, os.path.join("AMOS22-CT", "Masks")) + else: + shutil.move(f, os.path.join("AMOS22-MRI", "Masks")) + + # Move folder to dataset_dir + folders_to_move = [ + "AMOS22-CT", + "AMOS22-MRI", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/AMOS22/preprocess_detection.py b/src/medvision_ds/datasets/AMOS22/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..fca5da4ce902e524280ad63326b6a6e96b067aba --- /dev/null +++ b/src/medvision_ds/datasets/AMOS22/preprocess_detection.py @@ -0,0 +1,152 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "AMOS22", + "dataset_website": "https://amos22.grand-challenge.org", + "dataset_data": [ + "https://zenodo.org/records/7262581", + ], + "license": ["CC BY 4.0"], + "paper": ["https://doi.org/10.48550/arXiv.2206.08023"], +} + +labels_map = { + "1": "spleen", + "2": "right kidney", + "3": "left kidney", + "4": "gall bladder", + "5": "esophagus", + "6": "liver", + "7": "stomach", + "8": "arota", + "9": "postcava", + "10": "pancreas", + "11": "right adrenal gland", + "12": "left adrenal gland", + "13": "duodenum", + "14": "bladder", + "15": "prostate/uterus", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "AMOS22-CT/Images", + "mask_folder": "AMOS22-CT/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + { + "image_modality": "MRI", + "image_description": "abdominal magnetic resonance imaging (MRI) scan", + "image_folder": "AMOS22-MRI/Images", + "mask_folder": "AMOS22-MRI/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/AMOS22/preprocess_segmentation.py b/src/medvision_ds/datasets/AMOS22/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..0583819ea1e1e8e943bee369f8007d9acf232238 --- /dev/null +++ b/src/medvision_ds/datasets/AMOS22/preprocess_segmentation.py @@ -0,0 +1,152 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "AMOS22", + "dataset_website": "https://amos22.grand-challenge.org", + "dataset_data": [ + "https://zenodo.org/records/7262581", + ], + "license": ["CC BY 4.0"], + "paper": ["https://doi.org/10.48550/arXiv.2206.08023"], +} + +labels_map = { + "1": "spleen", + "2": "right kidney", + "3": "left kidney", + "4": "gall bladder", + "5": "esophagus", + "6": "liver", + "7": "stomach", + "8": "aorta", + "9": "postcava", + "10": "pancreas", + "11": "right adrenal gland", + "12": "left adrenal gland", + "13": "duodenum", + "14": "bladder", + "15": "prostate/uterus", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "AMOS22-CT/Images", + "mask_folder": "AMOS22-CT/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + { + "image_modality": "MRI", + "image_description": "abdominal magnetic resonance imaging (MRI) scan", + "image_folder": "AMOS22-MRI/Images", + "mask_folder": "AMOS22-MRI/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/__init__.py b/src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/download_raw.py b/src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/download_raw.py new file mode 100644 index 0000000000000000000000000000000000000000..38f6c71282bc368ee444d806da4044435904d3c6 --- /dev/null +++ b/src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/download_raw.py @@ -0,0 +1,161 @@ +import os +import shutil +import argparse +import glob +import nibabel as nib +from tqdm import tqdm +from huggingface_hub import snapshot_download +from medvision_ds.utils.preprocess_utils import move_folder, _get_cgroup_limited_cpus +from medvision_ds.utils.data_conversion import convert_mask_to_uint16_per_dir, copy_img_header_to_mask +from medvision_ds.datasets.AbdomenAtlas__1_0__Mini.preprocess_segmentation import ( + benchmark_plan as AbdomenAtlas1_0_Mini_benchmark_plan, +) + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: AbdomenAtlas1.0Mini +# Website: https://github.com/MrGiovanni/AbdomenAtlas +# Data: https://huggingface.co/datasets/AbdomenAtlas/AbdomenAtlas1.0Mini +# Format: nii.gz +# ==================================== + + +def convert_masks_to_uint16(dataset_dir): + mask_folders = _get_mask_folders(AbdomenAtlas1_0_Mini_benchmark_plan) + for folder in mask_folders: + mask_folder = os.path.join(dataset_dir, folder) + available_cpus = _get_cgroup_limited_cpus() + convert_mask_to_uint16_per_dir(mask_folder, workers_limit=available_cpus) + + + +def wrapper_copy_img_header_to_mask(img_files, mask_dir): + available_cpus = _get_cgroup_limited_cpus() + copy_img_header_to_mask(img_files, mask_dir, workers_limit=available_cpus) + + +def _get_mask_folders(bm_plan): + """Get unique mask folders from tasks""" + mask_folders = [] + for task in bm_plan["tasks"]: + mask_folders.append(task["mask_folder"]) + return list(set(mask_folders)) + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download dataset + dest_dir = "Images-raw" + snapshot_download( + repo_id="AbdomenAtlas/AbdomenAtlas1.0Mini", + repo_type="dataset", + local_dir=dest_dir, + revision="4dff62f03f7e4f17cd8c62617bc75fde9893a1e9", # commit hash on 2025-02-20 + max_workers=kwargs.get('max_workers', 1), + ) + + # Create Images and Masks directories + os.makedirs("Images", exist_ok=True) + os.makedirs("Masks", exist_ok=True) + + # Process each case folder + for case_dir in glob.glob(os.path.join(dest_dir, "BDMAP*")): + if os.path.isdir(case_dir): + case_name = os.path.basename(case_dir) + # Move CT images + ct_src = os.path.join(case_dir, "ct.nii.gz") + if os.path.exists(ct_src): + shutil.move(ct_src, os.path.join("Images", f"{case_name}.nii.gz")) + # Move mask files + mask_src = os.path.join(case_dir, "combined_labels.nii.gz") + if os.path.exists(mask_src): + shutil.move(mask_src, os.path.join("Masks", f"{case_name}.nii.gz")) + + # Copy Nifti header of images to masks (Multiprocessing) + print("Copying Nifti headers from images to masks...") + img_files = list(glob.glob(os.path.join("Images", "*.nii.gz"))) + wrapper_copy_img_header_to_mask(img_files, "Masks") + + # Convert masks to uint16 (Multiprocessing) + convert_masks_to_uint16(tmp_dir) + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--max_workers", + type=int, + default=1, + help="Maximum number of workers for download", + ) + args = parser.parse_args() + + # Extract known arguments and pass the rest as kwargs + kwargs = {"max_workers": args.max_workers} + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + **kwargs + ) diff --git a/src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/preprocess_detection.py b/src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..2b19ea72c3a2f4179d3589805e5f3c17371bde82 --- /dev/null +++ b/src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/preprocess_detection.py @@ -0,0 +1,136 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change: +# - keys in benchmark_plan +# - variable names: dataset_info, labels_map, benchmark_plan +# ==================================== +dataset_info = { + "dataset": "AbdomenAtlas1.0Mini", + "dataset_website": "https://github.com/MrGiovanni/AbdomenAtlas", + "dataset_data": [ + "https://huggingface.co/datasets/AbdomenAtlas/AbdomenAtlas1.0Mini" + ], + "license": ["CC BY-NC-SA 4.0"], + "paper": ["https://doi.org/10.1016/j.media.2024.103285"], +} + +labels_map = { + "1": "aorta", + "2": "gallbladder", + "3": "left kidney", + "4": "right kidney", + "5": "liver", + "6": "pancreas", + "7": "postcava (inferior vena cava)", + "8": "spleen", + "9": "stomach", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "abdominal CT scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/preprocess_segmentation.py b/src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..e7d20fc8d31795ee0f3aa681c04b91f70d9b81ff --- /dev/null +++ b/src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/preprocess_segmentation.py @@ -0,0 +1,137 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import ( + MedVision_BenchmarkPlannerSegmentation, +) + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "AbdomenAtlas1.0Mini", + "dataset_website": "https://github.com/MrGiovanni/AbdomenAtlas", + "dataset_data": [ + "https://huggingface.co/datasets/AbdomenAtlas/AbdomenAtlas1.0Mini" + ], + "license": ["CC BY-NC-SA 4.0"], + "paper": ["https://doi.org/10.1016/j.media.2024.103285"], +} + +labels_map = { + "1": "aorta", + "2": "gallbladder", + "3": "left kidney", + "4": "right kidney", + "5": "liver", + "6": "pancreas", + "7": "postcava (inferior vena cava)", + "8": "spleen", + "9": "stomach", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "abdominal CT scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/AbdomenCT_1K/__init__.py b/src/medvision_ds/datasets/AbdomenCT_1K/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/AbdomenCT_1K/download.py b/src/medvision_ds/datasets/AbdomenCT_1K/download.py new file mode 100644 index 0000000000000000000000000000000000000000..5381a86bd1f45927cd8a08c008a17c3e5f653daa --- /dev/null +++ b/src/medvision_ds/datasets/AbdomenCT_1K/download.py @@ -0,0 +1,127 @@ +import os +import shutil +import argparse +import zipfile +import urllib.request +import py7zr +from medvision_ds.utils.preprocess_utils import match_and_clean_files, move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: AbdomenCT-1K +# Website: https://github.com/JunMa11/AbdomenCT-1K +# Data: https://zenodo.org/records/5903099; https://zenodo.org/records/5903846; https://zenodo.org/records/5903769 +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Downloading dataset + urls = [ + "https://zenodo.org/records/5903099/files/AbdomenCT-1K-ImagePart1.zip?download=1", + "https://zenodo.org/records/5903846/files/AbdomenCT-1K-ImagePart2.zip?download=1", + "https://zenodo.org/records/5903769/files/AbdomenCT-1K-ImagePart3.zip?download=1", + "https://zenodo.org/records/5903769/files/Mask.7z?download=1", + ] + filenames = [ + "AbdomenCT-1K-ImagePart1.zip", + "AbdomenCT-1K-ImagePart2.zip", + "AbdomenCT-1K-ImagePart3.zip", + "Mask.7z", + ] + # Download files using urllib + for url, filename in zip(urls, filenames): + print(f"Downloading {filename}...") + urllib.request.urlretrieve(url, filename) + # Extract zip archives + for filename in filenames[:3]: + print(f"Extracting {filename}...") + with zipfile.ZipFile(filename, "r") as zip_ref: + zip_ref.extractall() + # Extract 7z archive + print(f"Extracting Mask.7z...") + with py7zr.SevenZipFile("Mask.7z", "r") as archive: + archive.extractall(path="Masks") + + # Combine data + for part in ["AbdomenCT-1K-ImagePart2", "AbdomenCT-1K-ImagePart3"]: + for file in os.listdir(part): + if file.endswith(".nii.gz"): + shutil.move(os.path.join(part, file), "AbdomenCT-1K-ImagePart1") + shutil.rmtree("Images") if os.path.exists("Images") else None + shutil.move("AbdomenCT-1K-ImagePart1", "Images") + + # Removing image files without corresponding mask files + match_and_clean_files("Images", "Masks") + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/AbdomenCT_1K/preprocess_detection.py b/src/medvision_ds/datasets/AbdomenCT_1K/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..c9990cb717185bc07ed5a5cf33da3bc06a8188e9 --- /dev/null +++ b/src/medvision_ds/datasets/AbdomenCT_1K/preprocess_detection.py @@ -0,0 +1,131 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "AbdomenCT-1K", + "dataset_website": "https://github.com/JunMa11/AbdomenCT-1K", + "dataset_data": [ + "https://zenodo.org/records/5903099", + "https://zenodo.org/records/5903846", + "https://zenodo.org/records/5903769", + ], + "license": ["CC BY 4.0"], + "paper": ["https://doi.org/10.1109/TPAMI.2021.3100536"], +} + +labels_map = { + "1": "liver", + "2": "kidney", + "3": "spleen", + "4": "pancreas", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/AbdomenCT_1K/preprocess_segmentation.py b/src/medvision_ds/datasets/AbdomenCT_1K/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..06daa5d8d7548dfa22f57fe928326d54e960b29d --- /dev/null +++ b/src/medvision_ds/datasets/AbdomenCT_1K/preprocess_segmentation.py @@ -0,0 +1,131 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "AbdomenCT-1K", + "dataset_website": "https://github.com/JunMa11/AbdomenCT-1K", + "dataset_data": [ + "https://zenodo.org/records/5903099", + "https://zenodo.org/records/5903846", + "https://zenodo.org/records/5903769", + ], + "license": ["CC BY 4.0"], + "paper": ["https://doi.org/10.1109/TPAMI.2021.3100536"], +} + +labels_map = { + "1": "liver", + "2": "kidney", + "3": "spleen", + "4": "pancreas", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/BCV15/__init__.py b/src/medvision_ds/datasets/BCV15/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/BCV15/download.py b/src/medvision_ds/datasets/BCV15/download.py new file mode 100644 index 0000000000000000000000000000000000000000..0267ec76664a32356728924e7a4ea27be06472f9 --- /dev/null +++ b/src/medvision_ds/datasets/BCV15/download.py @@ -0,0 +1,147 @@ +import os +import shutil +import synapseclient +import zipfile +import argparse +from medvision_ds.utils.preprocess_utils import move_folder + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: BCV15 +# Challenge: https://www.synapse.org/Synapse:syn3193805/wiki/89480 +# Data: Abdomen: https://www.synapse.org/Synapse:syn3193805/wiki/217789; +# Cervix: https://www.synapse.org/Synapse:syn3193805/wiki/217790 +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Initialize Synapse client + syn = synapseclient.Synapse() + token = os.environ.get("SYNAPSE_TOKEN") + if not token: + raise ValueError("SYNAPSE_TOKEN environment variable not set") + syn.login(authToken=token) + + # Download datasets + syn.get("syn3379050", downloadLocation="Abdomen") + syn.get("syn3546986", downloadLocation="Cervix") + + # Extract zip files + with zipfile.ZipFile(os.path.join("Abdomen", "RawData.zip"), "r") as zip_ref: + zip_ref.extractall("Abdomen") + with zipfile.ZipFile(os.path.join("Cervix", "CervixRawData.zip"), "r") as zip_ref: + zip_ref.extractall("Cervix") + + # Create directories and move files for Abdomen + os.makedirs(os.path.join("Abdomen", "Images"), exist_ok=True) + os.makedirs(os.path.join("Abdomen", "Masks"), exist_ok=True) + for f in os.listdir(os.path.join("Abdomen", "RawData", "Training", "img")): + if f.endswith(".nii.gz"): + shutil.move( + os.path.join("Abdomen", "RawData", "Training", "img", f), + os.path.join("Abdomen", "Images", f), + ) + for f in os.listdir(os.path.join("Abdomen", "RawData", "Training", "label")): + if f.endswith(".nii.gz"): + shutil.move( + os.path.join("Abdomen", "RawData", "Training", "label", f), + os.path.join("Abdomen", "Masks", f), + ) + + # Create directories and move files for Cervix + os.makedirs(os.path.join("Cervix", "Images"), exist_ok=True) + os.makedirs(os.path.join("Cervix", "Masks"), exist_ok=True) + for f in os.listdir(os.path.join("Cervix", "RawData", "Training", "img")): + if f.endswith(".nii.gz"): + shutil.move( + os.path.join("Cervix", "RawData", "Training", "img", f), + os.path.join("Cervix", "Images", f), + ) + for f in os.listdir(os.path.join("Cervix", "RawData", "Training", "label")): + if f.endswith(".nii.gz"): + shutil.move( + os.path.join("Cervix", "RawData", "Training", "label", f), + os.path.join("Cervix", "Masks", f), + ) + + # Rename final directories + os.rename("Abdomen", "BCV15-Abdomen") + os.rename("Cervix", "BCV15-Cervix") + + # Clean up raw data folders and zip files + shutil.rmtree(os.path.join("BCV15-Abdomen", "RawData"), ignore_errors=True) + os.remove(os.path.join("BCV15-Abdomen", "RawData.zip")) + shutil.rmtree(os.path.join("BCV15-Cervix", "RawData"), ignore_errors=True) + os.remove(os.path.join("BCV15-Cervix", "CervixRawData.zip")) + + # Move folder to dataset_dir + folders_to_move = [ + "BCV15-Cervix", + "BCV15-Abdomen", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/BCV15/preprocess_detection.py b/src/medvision_ds/datasets/BCV15/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..b314023090bcc0b7096ecc9d496d1e352c3c52d1 --- /dev/null +++ b/src/medvision_ds/datasets/BCV15/preprocess_detection.py @@ -0,0 +1,159 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "BCV15", + "dataset_website": "https://www.synapse.org/Synapse:syn3193805/wiki/89480", + "dataset_data": [ + "https://www.synapse.org/Synapse:syn3193805/wiki/217789", # Abdomen data + "https://www.synapse.org/Synapse:syn3193805/wiki/217790", # Cervix data + ], + "license": [""], + "paper": [""], +} + +labels_map_Abdomen = { + "1": "spleen", + "2": "right kidney", + "3": "left kidney", + "4": "gallbladder", + "5": "esophagus", + "6": "liver", + "7": "stomach", + "8": "aorta", + "9": "inferior vena cava", + "10": "portal vein and splenic vein", + "11": "pancreas", + "12": "right adrenal gland", + "13": "left adrenal gland", +} + +labels_map_Cervix = { + "1": "bladder", + "2": "uterus", + "3": "rectum", + "4": "small bowel", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "BCV15-Abdomen/Images", + "mask_folder": "BCV15-Abdomen/Masks", + "image_prefix": "img", + "image_suffix": ".nii.gz", + "mask_prefix": "label", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Abdomen, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "BCV15-Cervix/Images", + "mask_folder": "BCV15-Cervix/Masks", + "image_prefix": "", + "image_suffix": "-Image.nii.gz", + "mask_prefix": "", + "mask_suffix": "-Mask.nii.gz", + "labels_map": labels_map_Cervix, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/BCV15/preprocess_segmentation.py b/src/medvision_ds/datasets/BCV15/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..c01c6446364a4abcc641ff48c5d370e02262f90e --- /dev/null +++ b/src/medvision_ds/datasets/BCV15/preprocess_segmentation.py @@ -0,0 +1,159 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "BCV15", + "dataset_website": "https://www.synapse.org/Synapse:syn3193805/wiki/89480", + "dataset_data": [ + "https://www.synapse.org/Synapse:syn3193805/wiki/217789", # Abdomen data + "https://www.synapse.org/Synapse:syn3193805/wiki/217790", # Cervix data + ], + "license": [""], + "paper": [""], +} + +labels_map_Abdomen = { + "1": "spleen", + "2": "right kidney", + "3": "left kidney", + "4": "gallbladder", + "5": "esophagus", + "6": "liver", + "7": "stomach", + "8": "aorta", + "9": "inferior vena cava", + "10": "portal vein and splenic vein", + "11": "pancreas", + "12": "right adrenal gland", + "13": "left adrenal gland", +} + +labels_map_Cervix = { + "1": "bladder", + "2": "uterus", + "3": "rectum", + "4": "small bowel", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "BCV15-Abdomen/Images", + "mask_folder": "BCV15-Abdomen/Masks", + "image_prefix": "img", + "image_suffix": ".nii.gz", + "mask_prefix": "label", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Abdomen, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "BCV15-Cervix/Images", + "mask_folder": "BCV15-Cervix/Masks", + "image_prefix": "", + "image_suffix": "-Image.nii.gz", + "mask_prefix": "", + "mask_suffix": "-Mask.nii.gz", + "labels_map": labels_map_Cervix, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/BraTS24/__init__.py b/src/medvision_ds/datasets/BraTS24/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/BraTS24/download.py b/src/medvision_ds/datasets/BraTS24/download.py new file mode 100644 index 0000000000000000000000000000000000000000..4d677a988381ae728a4054d28c48f37ac9f8b12d --- /dev/null +++ b/src/medvision_ds/datasets/BraTS24/download.py @@ -0,0 +1,286 @@ +import os +import shutil +import argparse +import glob +import zipfile +import rarfile +import synapseclient +from medvision_ds.utils.preprocess_utils import process_dataset_mm, move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: BraTS24 +# Challenge: https://www.synapse.org/Synapse:syn53708249/wiki/ +# Segmentation Task: +# Adult Glioma Post Treatment (GLI): https://www.synapse.org/Synapse:syn53708249/wiki/627500 +# Brain Metastases (MET): https://www.synapse.org/Synapse:syn53708249/wiki/627504 +# Meningioma Radiotherapy (MEN-RT): https://www.synapse.org/Synapse:syn53708249/wiki/627503 +# Pediatric Tumors (PED): https://www.synapse.org/Synapse:syn53708249/wiki/627505 +# Data: +# Adult Glioma Post Treatment (GLI): https://www.synapse.org/Synapse:syn59059776 +# Brain Metastases (MET): https://www.synapse.org/Synapse:syn59059764 +# Meningioma Radiotherapy (MEN-RT): https://www.synapse.org/Synapse:syn59059779 +# Pediatric Tumors (PED): https://www.synapse.org/Synapse:syn58894466 +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Initialize Synapse client + syn = synapseclient.Synapse() + token = os.environ.get("SYNAPSE_TOKEN") + if not token: + raise ValueError("SYNAPSE_TOKEN environment variable not set") + syn.login(authToken=token) + + # Process GLI dataset + print("Downloading BraTS24-GLI dataset...") + os.makedirs("BraTS24-GLI", exist_ok=True) + for id in ["syn64314352", "syn60086071"]: + file = syn.get(id, downloadLocation="BraTS24-GLI") + print(f" - Downloaded: {file.path}") + with zipfile.ZipFile(file.path, "r") as zip_ref: + zip_ref.extractall("BraTS24-GLI") + os.chdir("BraTS24-GLI") + os.makedirs("Masks", exist_ok=True) + for mod in ["t1c", "t1n", "t2f", "t2w"]: + os.makedirs(f"Images-{mod}", exist_ok=True) + # Process GLI files + process_dataset_mm( + ["training_data1_v2", "training_data_additional"], + "*-seg.nii.gz", + ["t1c", "t1n", "t2f", "t2w"], + "-seg.nii.gz", + ) + # Cleanup GLI + for folder in ["training_data1_v2", "training_data_additional"]: + if os.path.exists(folder): + shutil.rmtree(folder) + for ext in ["tsv", "bib", "xlsx", "zip"]: + for f in glob.glob(f"*.{ext}"): + os.remove(f) + os.chdir("..") + + # Process MEN-RT dataset + print("Downloading BraTS24-MEN-RT dataset...") + os.makedirs("BraTS24-MEN-RT", exist_ok=True) + for id in ["syn60085033"]: + file = syn.get(id, downloadLocation="BraTS24-MEN-RT") + print(f" - Downloaded: {file.path}") + with zipfile.ZipFile(file.path, "r") as zip_ref: + zip_ref.extractall("BraTS24-MEN-RT") + os.chdir("BraTS24-MEN-RT") + os.makedirs("Masks", exist_ok=True) + os.makedirs("Images-t1c", exist_ok=True) + # Process MEN-RT files + process_dataset_mm( + ["BraTS-MEN-RT-Train-v2"], + "*_gtv.nii.gz", + ["t1c"], + "_gtv.nii.gz", + ) + # Cleanup MEN-RT + if os.path.exists("BraTS-MEN-RT-Train-v2"): + shutil.rmtree("BraTS-MEN-RT-Train-v2") + for ext in ["tsv", "bib", "zip"]: + for f in glob.glob(f"*.{ext}"): + os.remove(f) + os.chdir("..") + + # Process MET dataset + os.makedirs("BraTS24-MET", exist_ok=True) + # Download training data + for id in ["syn59407686", "syn59860022", "syn61596964"]: + file = syn.get(id, downloadLocation="BraTS24-MET") + print(f" - Downloaded: {file.path}") + with zipfile.ZipFile( + os.path.join( + "BraTS24-MET", "MICCAI-BraTS2024-MET-Challenge-TrainingData_1.zip" + ), + "r", + ) as zip_ref: + zip_ref.extractall("BraTS24-MET") + with zipfile.ZipFile( + os.path.join( + "BraTS24-MET", "MICCAI-BraTS2024-MET-Challenge-TrainingData_2.zip" + ), + "r", + ) as zip_ref: + zip_ref.extractall( + os.path.join("BraTS24-MET", "MICCAI-BraTS2024-MET-Challenge-TrainingData_2") + ) + with zipfile.ZipFile( + os.path.join( + "BraTS24-MET", + "MICCAI-BraTS2024-MET-Challenge-TrainingData_2-fixed-cases.zip", + ), + "r", + ) as zip_ref: + zip_ref.extractall("BraTS24-MET") + syn.get("syn61929632", downloadLocation="BraTS24-MET") + rar_path = os.path.join("BraTS24-MET", "BraTS-MET-00232-000.rar") + extract_dir = os.path.join( + "BraTS24-MET", "MICCAI-BraTS2024-MET-Challenge-TrainingData_2-fixed-cases" + ) + os.makedirs(extract_dir, exist_ok=True) + with rarfile.RarFile(rar_path) as rf: + rf.extractall(path=extract_dir) + os.chdir("BraTS24-MET") + # Create directories + os.makedirs("Masks", exist_ok=True) + for mod in ["t1c", "t1n", "t2f", "t2w"]: + os.makedirs(f"Images-{mod}", exist_ok=True) + # Process main training datasets + process_dataset_mm( + [ + "MICCAI-BraTS2024-MET-Challenge-Training_1", + "MICCAI-BraTS2024-MET-Challenge-TrainingData_2", + ], + "*-seg.nii.gz", + ["t1c", "t1n", "t2f", "t2w"], + "-seg.nii.gz", + ) + # Process fixed cases with force overwrite + process_dataset_mm( + ["MICCAI-BraTS2024-MET-Challenge-TrainingData_2-fixed-cases"], + "*-seg.nii.gz", + ["t1c", "t1n", "t2f", "t2w"], + "-seg.nii.gz", + replace=True, # Enable force overwrite + ) + # Delete cases where the NIfTI image and mask headers don't match + cases_to_remove = ["BraTS-MET-00232-000"] + for case in cases_to_remove: + for mod in ["t1c", "t1n", "t2f", "t2w"]: + img_path = os.path.join( + "BraTS24-MET", f"Images-{mod}", f"{case}-{mod}.nii.gz" + ) + mask_path = os.path.join("BraTS24-MET", "Masks", f"{case}-seg.nii.gz") + if os.path.exists(img_path): + os.remove(img_path) + if os.path.exists(mask_path): + os.remove(mask_path) + # Cleanup + for folder in [ + "MICCAI-BraTS2024-MET-Challenge-TrainingData_2-fixed-cases", + "MICCAI-BraTS2024-MET-Challenge-Training_1", + "MICCAI-BraTS2024-MET-Challenge-TrainingData_2", + ]: + if os.path.exists(folder): + shutil.rmtree(folder) + for ext in ["tsv", "bib", "zip", "rar"]: + for f in glob.glob(f"*.{ext}"): + os.remove(f) + os.chdir("..") + + # Process PED dataset + os.makedirs("BraTS24-PED", exist_ok=True) + # Download training data + for id in ["syn58894928", "syn60140557"]: + file = syn.get(id, downloadLocation="BraTS24-PED") + print(f" - Downloaded: {file.path}") + with zipfile.ZipFile(file.path, "r") as zip_ref: + zip_ref.extractall("BraTS24-PED") + os.chdir("BraTS24-PED") + # Fix broken case + if os.path.exists(os.path.join("BraTS-PEDs2024_Training", "BraTS-PED-00255-000")): + shutil.rmtree(os.path.join("BraTS-PEDs2024_Training", "BraTS-PED-00255-000")) + shutil.move("BraTS-PED-00255-000", "BraTS-PEDs2024_Training") + # Create directories + os.makedirs("Masks", exist_ok=True) + for mod in ["t1c", "t1n", "t2f", "t2w"]: + os.makedirs(f"Images-{mod}", exist_ok=True) + # Process training data + process_dataset_mm( + ["BraTS-PEDs2024_Training"], + "*-seg.nii.gz", + ["t1c", "t1n", "t2f", "t2w"], + "-seg.nii.gz", + ) + # Remove any hidden files + for hidden_file in glob.glob( + os.path.join("BraTS24-PED", "**", "._*.nii.gz"), recursive=True + ): + os.remove(hidden_file) + # Cleanup + for folder in ["BraTS-PEDs2024_Training", "__MACOSX"]: + if os.path.exists(folder): + shutil.rmtree(folder) + for ext in ["tsv", "bib", "zip"]: + for f in glob.glob(f"*.{ext}"): + os.remove(f) + os.chdir("..") + + # Move folder to dataset_dir + folders_to_move = [ + "BraTS24-GLI", + "BraTS24-MEN-RT", + "BraTS24-MET", + "BraTS24-PED", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/BraTS24/preprocess_biometry.py b/src/medvision_ds/datasets/BraTS24/preprocess_biometry.py new file mode 100644 index 0000000000000000000000000000000000000000..b6217e4fa6736feaf907e2d4f261dee6f4ae6b2a --- /dev/null +++ b/src/medvision_ds/datasets/BraTS24/preprocess_biometry.py @@ -0,0 +1,463 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerBiometry_fromSeg + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +CLUSTER_SIZE_THRESHOLD = 200 + +dataset_info = { + "dataset": "", + "dataset_website": "https://www.synapse.org/Synapse:syn53708249/wiki/", + "dataset_data": [ + "https://www.synapse.org/Synapse:syn59059776", # GLI + "https://www.synapse.org/Synapse:syn59059764", # MET + "https://www.synapse.org/Synapse:syn59059779", # MEN-RT + "https://www.synapse.org/Synapse:syn58894466", # PED + ], + "license": [""], + "paper": [ + "https://doi.org/10.48550/arXiv.2405.18368", # GLI + "", # MET + "https://doi.org/10.48550/arXiv.2405.18383", # MEN-RT + "https://doi.org/10.48550/arXiv.2404.15009", # PED + ], +} + +labels_map_GLI = { + "1": "non-enhancing brain tumor core", + "2": "surrounding non-enhancing flair hyperintensity of brain", + "3": "enhancing brain tumor tissue", + "4": "resection cavity of brain", +} + +labels_map_MET = { + "1": "non-enhancing brain tumor core", + "2": "surrounding non-enhancing flair hyperintensity of brain", + "3": "enhancing brain tumor tissue", +} + +labels_map_MEN_RT = {"1": "gross tumor volume of brain"} + +labels_map_PED = { + "1": "enhancing brain tumor", + "2": "non-enhancing brain tumor", + "3": "cystic component of brain", + "4": "peritumoral edema of brain", +} +# ==================================== + + +# =============== +# DO NOT CHANGE +# =============== +landmarks_map = { + "P1": "most right/anterior/superior endpoint of the major axis", + "P2": "most left/superior/inferior endpoint of the major axis", + "P3": "most right/anterior/superior endpoint of the minor axis", + "P4": "most left/superior/inferior endpoint of the minor axis", +} + +lines_map = { + "L-1-2": { + "name": "marjor axis of the fitted ellipse", + "element_keys": ["P1", "P2"], + "element_map_name": "landmarks_map", + }, + "L-3-4": { + "name": "minor axis of the fitted ellipse", + "element_keys": ["P3", "P4"], + "element_map_name": "landmarks_map", + }, +} + +angles_map = {} + +biometrics_map = [ + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-1-2", + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-3-4", + }, +] +# =============== + + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "T2 Fluid Attenuated Inversion Recovery (FLAIR) brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-GLI/Images-t2f", + "mask_folder": "BraTS24-GLI/Masks", + "landmark_folder": "BraTS24-GLI/Landmarks-Label1", + "landmark_figure_folder": "BraTS24-GLI/Landmarks-Label1-fig", + "image_prefix": "", + "image_suffix": "-t2f.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_GLI, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 1, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "T2 Fluid Attenuated Inversion Recovery (FLAIR) brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-GLI/Images-t2f", + "mask_folder": "BraTS24-GLI/Masks", + "landmark_folder": "BraTS24-GLI/Landmarks-Label2", + "landmark_figure_folder": "BraTS24-GLI/Landmarks-Label2-fig", + "image_prefix": "", + "image_suffix": "-t2f.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_GLI, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 2, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "T2 Fluid Attenuated Inversion Recovery (FLAIR) brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-GLI/Images-t2f", + "mask_folder": "BraTS24-GLI/Masks", + "landmark_folder": "BraTS24-GLI/Landmarks-Label3", + "landmark_figure_folder": "BraTS24-GLI/Landmarks-Label3-fig", + "image_prefix": "", + "image_suffix": "-t2f.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_GLI, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 3, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "T2 Fluid Attenuated Inversion Recovery (FLAIR) brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-GLI/Images-t2f", + "mask_folder": "BraTS24-GLI/Masks", + "landmark_folder": "BraTS24-GLI/Landmarks-Label4", + "landmark_figure_folder": "BraTS24-GLI/Landmarks-Label4-fig", + "image_prefix": "", + "image_suffix": "-t2f.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_GLI, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 4, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "contrast enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-MEN-RT/Images-t1c", + "mask_folder": "BraTS24-MEN-RT/Masks", + "landmark_folder": "BraTS24-MEN-RT/Landmarks-Label1", + "landmark_figure_folder": "BraTS24-MEN-RT/Landmarks-Label1-fig", + "image_prefix": "", + "image_suffix": "_t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "_gtv.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_MEN_RT, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 1, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "contrast enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-MET/Images-t1c", + "mask_folder": "BraTS24-MET/Masks", + "landmark_folder": "BraTS24-MET/Landmarks-Label1", + "landmark_figure_folder": "BraTS24-MET/Landmarks-Label1-fig", + "image_prefix": "", + "image_suffix": "-t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_MET, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 1, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "contrast enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-MET/Images-t1c", + "mask_folder": "BraTS24-MET/Masks", + "landmark_folder": "BraTS24-MET/Landmarks-Label2", + "landmark_figure_folder": "BraTS24-MET/Landmarks-Label2-fig", + "image_prefix": "", + "image_suffix": "-t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_MET, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 2, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "contrast enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-MET/Images-t1c", + "mask_folder": "BraTS24-MET/Masks", + "landmark_folder": "BraTS24-MET/Landmarks-Label3", + "landmark_figure_folder": "BraTS24-MET/Landmarks-Label3-fig", + "image_prefix": "", + "image_suffix": "-t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_MET, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 3, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "contrast enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-PED/Images-t1c", + "mask_folder": "BraTS24-PED/Masks", + "landmark_folder": "BraTS24-PED/Landmarks-Label1", + "landmark_figure_folder": "BraTS24-PED/Landmarks-Label1-fig", + "image_prefix": "", + "image_suffix": "-t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_PED, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 1, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "contrast enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-PED/Images-t1c", + "mask_folder": "BraTS24-PED/Masks", + "landmark_folder": "BraTS24-PED/Landmarks-Label2", + "landmark_figure_folder": "BraTS24-PED/Landmarks-Label2-fig", + "image_prefix": "", + "image_suffix": "-t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_PED, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 2, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "contrast enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-PED/Images-t1c", + "mask_folder": "BraTS24-PED/Masks", + "landmark_folder": "BraTS24-PED/Landmarks-Label3", + "landmark_figure_folder": "BraTS24-PED/Landmarks-Label3-fig", + "image_prefix": "", + "image_suffix": "-t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_PED, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 3, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "contrast enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-PED/Images-t1c", + "mask_folder": "BraTS24-PED/Masks", + "landmark_folder": "BraTS24-PED/Landmarks-Label4", + "landmark_figure_folder": "BraTS24-PED/Landmarks-Label4-fig", + "image_prefix": "", + "image_suffix": "-t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_PED, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 4, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + shrunken_bbox_scale=0.9, + enlarged_bbox_scale=1.1, + force_uint16_mask=False, + reorient2RAS=False, + visualization=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerBiometry_fromSeg( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + shrunk_bbox_scale=shrunken_bbox_scale, + enlarged_bbox_scale=enlarged_bbox_scale, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + visualization=visualization, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for biometric measurement task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--shrunken_bbox_scale", + type=float, + default=0.9, + help="Scale factor for shrunken bounding box", + ) + parser.add_argument( + "--enlarged_bbox_scale", + type=float, + default=1.1, + help="Scale factor for enlarged bounding box", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + parser.add_argument( + "--visualization", + action="store_true", + help="Enable visualization of the dataset processing", + ) + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + shrunken_bbox_scale=args.shrunken_bbox_scale, + enlarged_bbox_scale=args.enlarged_bbox_scale, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + visualization=args.visualization, + ) diff --git a/src/medvision_ds/datasets/BraTS24/preprocess_detection.py b/src/medvision_ds/datasets/BraTS24/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..a3d50e05b1adf7e3474a7bfdae36e1898c257a0d --- /dev/null +++ b/src/medvision_ds/datasets/BraTS24/preprocess_detection.py @@ -0,0 +1,285 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "", + "dataset_website": "https://www.synapse.org/Synapse:syn53708249/wiki/", + "dataset_data": [ + "https://www.synapse.org/Synapse:syn59059776", # GLI + "https://www.synapse.org/Synapse:syn59059764", # MET + "https://www.synapse.org/Synapse:syn59059779", # MEN-RT + "https://www.synapse.org/Synapse:syn58894466", # PED + ], + "license": [""], + "paper": [ + "https://doi.org/10.48550/arXiv.2405.18368", # GLI + "", # MET + "https://doi.org/10.48550/arXiv.2405.18383", # MEN-RT + "https://doi.org/10.48550/arXiv.2404.15009", # PED + ], +} + +labels_map_GLI = { + "1": "non-enhancing brain tumor core", + "2": "surrounding non-enhancing flair hyperintensity of brain", + "3": "enhancing brain tumor tissue", + "4": "resection cavity of brain", +} + +labels_map_MET = { + "1": "non-enhancing brain tumor core", + "2": "surrounding non-enhancing flair hyperintensity of brain", + "3": "enhancing brain tumor tissue", +} + +labels_map_MEN_RT = {"1": "gross tumor volume of brain"} + +labels_map_PED = { + "1": "enhancing brain tumor", + "2": "non-enhancing brain tumor", + "3": "cystic component of brain", + "4": "peritumoral edema of brain", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "gadolinium-enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-GLI/Images-t1c", + "mask_folder": "BraTS24-GLI/Masks", + "image_prefix": "", + "image_suffix": "-t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_GLI, + }, + { + "image_modality": "MRI", + "image_description": "non-contrast T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-GLI/Images-t1n", + "mask_folder": "BraTS24-GLI/Masks", + "image_prefix": "", + "image_suffix": "-t1n.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_GLI, + }, + { + "image_modality": "MRI", + "image_description": "T2 Fluid Attenuated Inversion Recovery (FLAIR) brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-GLI/Images-t2f", + "mask_folder": "BraTS24-GLI/Masks", + "image_prefix": "", + "image_suffix": "-t2f.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_GLI, + }, + { + "image_modality": "MRI", + "image_description": "T2-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-GLI/Images-t2w", + "mask_folder": "BraTS24-GLI/Masks", + "image_prefix": "", + "image_suffix": "-t2w.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_GLI, + }, + { + "image_modality": "MRI", + "image_description": "contrast enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-MEN-RT/Images-t1c", + "mask_folder": "BraTS24-MEN-RT/Masks", + "image_prefix": "", + "image_suffix": "_t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "_gtv.nii.gz", + "labels_map": labels_map_MEN_RT, + }, + { + "image_modality": "MRI", + "image_description": "contrast enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-MET/Images-t1c", + "mask_folder": "BraTS24-MET/Masks", + "image_prefix": "", + "image_suffix": "-t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_MET, + }, + { + "image_modality": "MRI", + "image_description": "non-contrast T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-MET/Images-t1n", + "mask_folder": "BraTS24-MET/Masks", + "image_prefix": "", + "image_suffix": "-t1n.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_MET, + }, + { + "image_modality": "MRI", + "image_description": "T2 Fluid Attenuated Inversion Recovery (FLAIR) brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-MET/Images-t2f", + "mask_folder": "BraTS24-MET/Masks", + "image_prefix": "", + "image_suffix": "-t2f.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_MET, + }, + { + "image_modality": "MRI", + "image_description": "T2-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-MET/Images-t2w", + "mask_folder": "BraTS24-MET/Masks", + "image_prefix": "", + "image_suffix": "-t2w.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_MET, + }, + { + "image_modality": "MRI", + "image_description": "contrast enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-PED/Images-t1c", + "mask_folder": "BraTS24-PED/Masks", + "image_prefix": "", + "image_suffix": "-t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_PED, + }, + { + "image_modality": "MRI", + "image_description": "non-contrast T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-PED/Images-t1n", + "mask_folder": "BraTS24-PED/Masks", + "image_prefix": "", + "image_suffix": "-t1n.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_PED, + }, + { + "image_modality": "MRI", + "image_description": "T2 Fluid Attenuated Inversion Recovery (FLAIR) brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-PED/Images-t2f", + "mask_folder": "BraTS24-PED/Masks", + "image_prefix": "", + "image_suffix": "-t2f.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_PED, + }, + { + "image_modality": "MRI", + "image_description": "T2-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-PED/Images-t2w", + "mask_folder": "BraTS24-PED/Masks", + "image_prefix": "", + "image_suffix": "-t2w.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_PED, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/BraTS24/preprocess_segmentation.py b/src/medvision_ds/datasets/BraTS24/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..3423953576f01ec77424d71c311bafe7f16102b3 --- /dev/null +++ b/src/medvision_ds/datasets/BraTS24/preprocess_segmentation.py @@ -0,0 +1,285 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "", + "dataset_website": "https://www.synapse.org/Synapse:syn53708249/wiki/", + "dataset_data": [ + "https://www.synapse.org/Synapse:syn59059776", # GLI + "https://www.synapse.org/Synapse:syn59059764", # MET + "https://www.synapse.org/Synapse:syn59059779", # MEN-RT + "https://www.synapse.org/Synapse:syn58894466", # PED + ], + "license": [""], + "paper": [ + "https://doi.org/10.48550/arXiv.2405.18368", # GLI + "", # MET + "https://doi.org/10.48550/arXiv.2405.18383", # MEN-RT + "https://doi.org/10.48550/arXiv.2404.15009", # PED + ], +} + +labels_map_GLI = { + "1": "non-enhancing brain tumor core", + "2": "surrounding non-enhancing flair hyperintensity of brain", + "3": "enhancing brain tumor tissue", + "4": "resection cavity of brain", +} + +labels_map_MET = { + "1": "non-enhancing brain tumor core", + "2": "surrounding non-enhancing flair hyperintensity of brain", + "3": "enhancing brain tumor tissue", +} + +labels_map_MEN_RT = {"1": "gross tumor volume of brain"} + +labels_map_PED = { + "1": "enhancing brain tumor", + "2": "non-enhancing brain tumor", + "3": "cystic component of brain", + "4": "peritumoral edema of brain", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "gadolinium-enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-GLI/Images-t1c", + "mask_folder": "BraTS24-GLI/Masks", + "image_prefix": "", + "image_suffix": "-t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_GLI, + }, + { + "image_modality": "MRI", + "image_description": "non-contrast T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-GLI/Images-t1n", + "mask_folder": "BraTS24-GLI/Masks", + "image_prefix": "", + "image_suffix": "-t1n.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_GLI, + }, + { + "image_modality": "MRI", + "image_description": "T2 Fluid Attenuated Inversion Recovery (FLAIR) brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-GLI/Images-t2f", + "mask_folder": "BraTS24-GLI/Masks", + "image_prefix": "", + "image_suffix": "-t2f.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_GLI, + }, + { + "image_modality": "MRI", + "image_description": "T2-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-GLI/Images-t2w", + "mask_folder": "BraTS24-GLI/Masks", + "image_prefix": "", + "image_suffix": "-t2w.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_GLI, + }, + { + "image_modality": "MRI", + "image_description": "contrast enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-MEN-RT/Images-t1c", + "mask_folder": "BraTS24-MEN-RT/Masks", + "image_prefix": "", + "image_suffix": "_t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "_gtv.nii.gz", + "labels_map": labels_map_MEN_RT, + }, + { + "image_modality": "MRI", + "image_description": "contrast enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-MET/Images-t1c", + "mask_folder": "BraTS24-MET/Masks", + "image_prefix": "", + "image_suffix": "-t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_MET, + }, + { + "image_modality": "MRI", + "image_description": "non-contrast T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-MET/Images-t1n", + "mask_folder": "BraTS24-MET/Masks", + "image_prefix": "", + "image_suffix": "-t1n.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_MET, + }, + { + "image_modality": "MRI", + "image_description": "T2 Fluid Attenuated Inversion Recovery (FLAIR) brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-MET/Images-t2f", + "mask_folder": "BraTS24-MET/Masks", + "image_prefix": "", + "image_suffix": "-t2f.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_MET, + }, + { + "image_modality": "MRI", + "image_description": "T2-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-MET/Images-t2w", + "mask_folder": "BraTS24-MET/Masks", + "image_prefix": "", + "image_suffix": "-t2w.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_MET, + }, + { + "image_modality": "MRI", + "image_description": "contrast enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-PED/Images-t1c", + "mask_folder": "BraTS24-PED/Masks", + "image_prefix": "", + "image_suffix": "-t1c.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_PED, + }, + { + "image_modality": "MRI", + "image_description": "non-contrast T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-PED/Images-t1n", + "mask_folder": "BraTS24-PED/Masks", + "image_prefix": "", + "image_suffix": "-t1n.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_PED, + }, + { + "image_modality": "MRI", + "image_description": "T2 Fluid Attenuated Inversion Recovery (FLAIR) brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-PED/Images-t2f", + "mask_folder": "BraTS24-PED/Masks", + "image_prefix": "", + "image_suffix": "-t2f.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_PED, + }, + { + "image_modality": "MRI", + "image_description": "T2-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "BraTS24-PED/Images-t2w", + "mask_folder": "BraTS24-PED/Masks", + "image_prefix": "", + "image_suffix": "-t2w.nii.gz", + "mask_prefix": "", + "mask_suffix": "-seg.nii.gz", + "labels_map": labels_map_PED, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/CAMUS/__init__.py b/src/medvision_ds/datasets/CAMUS/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/CAMUS/download_fast.py b/src/medvision_ds/datasets/CAMUS/download_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..73a297319d6297ed56d578611559509f271a9cc1 --- /dev/null +++ b/src/medvision_ds/datasets/CAMUS/download_fast.py @@ -0,0 +1,103 @@ +import os +import zipfile +import shutil +import argparse +from huggingface_hub import hf_hub_download +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: CAMUS +# Challenge: https://www.creatis.insa-lyon.fr/Challenge/camus/ +# Data: https://humanheart-project.creatis.insa-lyon.fr/database/#collection/6373703d73e9f0047faa1bc8 +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download and extract dataset + for file in ["Images.zip", "Masks.zip"]: + # Download and extract dataset + hf_hub_download( + repo_id="YongchengYAO/CAMUS-Lite", + filename=file, + repo_type="dataset", + revision="b5f71984d0f1c11827a826e19e76cad02aa668ed", # commit hash on 2025-02-20 + local_dir=".", + ) + print(f"Extracting {file}... This may take some time.") + with zipfile.ZipFile(file, 'r') as zip_ref: + zip_ref.extractall() + os.remove(file) + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/CAMUS/download_raw.py b/src/medvision_ds/datasets/CAMUS/download_raw.py new file mode 100644 index 0000000000000000000000000000000000000000..607550f2dded99227b3aeebb152fab92a71e7c15 --- /dev/null +++ b/src/medvision_ds/datasets/CAMUS/download_raw.py @@ -0,0 +1,119 @@ +import os +import zipfile +import urllib.request +import shutil +import argparse +from medvision_ds.utils.preprocess_utils import process_dataset, move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: CAMUS +# Challenge: https://www.creatis.insa-lyon.fr/Challenge/camus/ +# Data: https://humanheart-project.creatis.insa-lyon.fr/database/#collection/6373703d73e9f0047faa1bc8 +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download dataset + urllib.request.urlretrieve( + "https://humanheart-project.creatis.insa-lyon.fr/database/api/v1/collection/6373703d73e9f0047faa1bc8/download", + "CAMUS.zip", + ) + + # Extract zip using zipfile + with zipfile.ZipFile("CAMUS.zip", "r") as zip_ref: + zip_ref.extractall() + # Move nifti database + shutil.move(os.path.join("CAMUS_public", "database_nifti"), ".") + + # Create directories + os.makedirs("Images", exist_ok=True) + os.makedirs("Masks", exist_ok=True) + + # Process dataset + process_dataset( + data_dirs=["database_nifti"], + seg_pattern="*_gt.nii.gz", + base_suffix="_gt.nii.gz", + ) + + # Remove 2D images and masks + for file in os.listdir("Images"): + if file.endswith("_ED.nii.gz") or file.endswith("_ES.nii.gz"): + os.remove(os.path.join("Images", file)) + for file in os.listdir("Masks"): + if file.endswith("_ED_gt.nii.gz") or file.endswith("_ES_gt.nii.gz"): + os.remove(os.path.join("Masks", file)) + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/CAMUS/preprocess_detection.py b/src/medvision_ds/datasets/CAMUS/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..2ba047ac6cec06d8a5996f5b8fe985135fe31f86 --- /dev/null +++ b/src/medvision_ds/datasets/CAMUS/preprocess_detection.py @@ -0,0 +1,129 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "CAMUS", + "dataset_website": "https://www.creatis.insa-lyon.fr/Challenge/camus/", + "dataset_data": [ + "https://humanheart-project.creatis.insa-lyon.fr/database/#collection/6373703d73e9f0047faa1bc8", + ], + "license": ["CC BY-NC-SA 4.0"], + "paper": ["https://doi.org/10.1109/TMI.2019.2900516"], +} + +labels_map = { + "1": "left ventricular myocardium", + "2": "left ventricle", + "3": "left atrium", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "ultrasound", + "image_description": "echocardiographic image", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": "_gt.nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/CAMUS/preprocess_segmentation.py b/src/medvision_ds/datasets/CAMUS/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..ec3e73471eb201b1ee04c4f7eb01065bc9349934 --- /dev/null +++ b/src/medvision_ds/datasets/CAMUS/preprocess_segmentation.py @@ -0,0 +1,129 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "CAMUS", + "dataset_website": "https://www.creatis.insa-lyon.fr/Challenge/camus/", + "dataset_data": [ + "https://humanheart-project.creatis.insa-lyon.fr/database/#collection/6373703d73e9f0047faa1bc8", + ], + "license": ["CC BY-NC-SA 4.0"], + "paper": ["https://doi.org/10.1109/TMI.2019.2900516"], +} + +labels_map = { + "1": "left ventricular myocardium", + "2": "left ventricle", + "3": "left atrium", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "ultrasound", + "image_description": "echocardiographic image", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": "_gt.nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/Ceph_Biometrics_400/__init__.py b/src/medvision_ds/datasets/Ceph_Biometrics_400/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/Ceph_Biometrics_400/download_fast.py b/src/medvision_ds/datasets/Ceph_Biometrics_400/download_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..b0bcb27ba53615716de7def1919ab2daa792d2a3 --- /dev/null +++ b/src/medvision_ds/datasets/Ceph_Biometrics_400/download_fast.py @@ -0,0 +1,105 @@ +import os +import zipfile +import shutil +import argparse +from huggingface_hub import hf_hub_download +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: Ceph-Biometrics-400 +# Data (original): https://figshare.com/s/37ec464af8e81ae6ebbf +# Data (HF): https://huggingface.co/datasets/YongchengYAO/Ceph-Biometrics-400 +# Format (original): bm +# Format (HF): nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download and extract dataset + for file in ["Images.zip", "Landmarks.zip", "Landmarks-fig.zip"]: + # Download and extract dataset + hf_hub_download( + repo_id="YongchengYAO/Ceph-Biometrics-400", + filename=file, + repo_type="dataset", + revision="8cd93443d4ba6d327c74dde39184d846034d920a", # commit hash on 2025-05-09 + local_dir=".", + ) + print(f"Extracting {file}... This may take some time.") + with zipfile.ZipFile(file, 'r') as zip_ref: + zip_ref.extractall() + os.remove(file) + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Landmarks", + "Landmarks-fig", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/Ceph_Biometrics_400/download_raw.py b/src/medvision_ds/datasets/Ceph_Biometrics_400/download_raw.py new file mode 100644 index 0000000000000000000000000000000000000000..292a760f8f2b65f358fb5573d81f114b54f8c898 --- /dev/null +++ b/src/medvision_ds/datasets/Ceph_Biometrics_400/download_raw.py @@ -0,0 +1,335 @@ +import os +import shutil +import glob +import json +import rarfile +import zipfile +import gzip +import urllib.request +import argparse +import nibabel as nib +import matplotlib.pyplot as plt +from medvision_ds.utils.data_conversion import convert_bmp_to_niigz +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: Ceph-Biometrics-400 +# Data (original): https://figshare.com/s/37ec464af8e81ae6ebbf +# Data (HF): https://huggingface.co/datasets/YongchengYAO/Ceph-Biometrics-400 +# Format (original): bm +# Format (HF): nii.gz +# ==================================== + + +def process_landmarks_data( + landmarks_txt_dir: str, + landmarks_json_dir: str, + n: int, + img_sizes, + flip_dim0=False, + flip_dim1=False, + swap_dim01=False, +) -> None: + """ + Read landmark points from all txt files in a directory and save as JSON files. + + Args: + in_dir (str): Directory containing the txt files + out_dir (str): Directory where JSON files will be saved + n (int): Number of lines to read from each file + height_width_orig: Original height and width of the image + swap_xy (bool): Whether to swap x and y coordinates + slip_x (bool): Whether to flip coordinates along x-axis + slip_y (bool): Whether to flip coordinates along y-axis + """ + ( + os.makedirs(landmarks_json_dir, exist_ok=True) + if not os.path.exists(landmarks_json_dir) + else None + ) + + for txt_file in glob.glob(os.path.join(landmarks_txt_dir, "*.txt")): + + landmarks = {} + filename = os.path.basename(txt_file) + json_path = os.path.join( + landmarks_json_dir, filename.replace(".txt", ".json.gz") + ) + + try: + with open(txt_file, "r") as f: + for i in range(n): + line = f.readline().strip() + if not line: + break + # Note: this is correct, DO NOT SWAP idx_dim0 and idx_dim1 + # Assuming an image with height and width: + # - The data array read from bmp file is of size (height, width) -- dim0 is height, dim1 is width + # - The landmark coordinates are defined as the indices in width (dim1) and height (dim0) directions + idx_dim1, idx_dim0 = map(int, line.split(",")) + + # Apply transformations + # Note: this is correct + # DO NOT SWAP the order of transformations + if flip_dim0: + idx_dim0 = img_sizes[0] - idx_dim0 + if flip_dim1: + idx_dim1 = img_sizes[1] - idx_dim1 + if swap_dim01: # this line should be AFTER slip_x and slip_y + idx_dim0, idx_dim1 = idx_dim1, idx_dim0 + + # Save landmark coordinates in 0-based indices + landmarks[f"P{i+1}"] = [ + coord - 1 for coord in [1, idx_dim0, idx_dim1] + ] + + # This data structure is designed to be compatible with biometric data constructed from segmentation masks + json_dict = { + "slice_landmarks_x": [ + { + "slice_idx": 0, + "landmarks": landmarks, + }, + ], + "slice_landmarks_y": [], + "slice_landmarks_z": [], + } + + # Save to JSON or compressed JSON + if json_path.endswith(".json.gz"): + with gzip.open(json_path, "wt") as f: + json.dump(json_dict, f, indent=4) + else: + with open(json_path, "w") as f: + json.dump(json_dict, f, indent=4) + + except FileNotFoundError: + print(f"Error: File {txt_file} not found") + except ValueError: + print(f"Error: Invalid format in file {txt_file}") + except Exception as e: + print(f"Error reading file {txt_file}: {str(e)}") + + +def plot_sagittal_slice_with_landmarks( + nii_path: str, json_path: str, fig_path: str = None +): + """Plot first slice from NIfTI file and overlay landmarks from JSON file. + + Args: + nii_path (str): Path to .nii.gz file + json_path (str): Path to landmarks JSON file + fig_path (str, optional): Path to save the plot. If None, displays plot + """ + # Load NIfTI image and extract first slice + nii_img = nib.load(nii_path) + slice_data = nii_img.get_fdata()[0, :, :] + + # Load landmark coordinates from JSON + if json_path.endswith(".json.gz"): + with gzip.open(json_path, "rt") as f: + landmarks_json = json.load(f) + else: + with open(json_path, "r") as f: + landmarks_json = json.load(f) + + # Setup visualization + plt.figure(figsize=(12, 12)) + plt.imshow( + slice_data.T, cmap="gray", origin="lower" + ) # the transpose is necessary only for visualization + + # Extract and plot landmark coordinates + coords_dim0 = [] + coords_dim1 = [] + landmarks = landmarks_json["slice_landmarks_x"][0]["landmarks"] + for point_id, coords in landmarks.items(): + if len(coords) == 3: # Check for valid [1, x, y] format + # Note: this is definitely correct, DO NOT SWAP coords[1] and coords[2] + coords_dim0.append(coords[1]) + coords_dim1.append(coords[2]) + + # Add landmarks and labels + plt.scatter( + coords_dim0, + coords_dim1, + facecolors="#18A727", + edgecolors="black", + marker="o", + s=80, + linewidth=1.5, + ) + for i, (x, y) in enumerate(zip(coords_dim0, coords_dim1), 1): + plt.annotate( + f"$\\mathbf{{{i}}}$", + (x, y), + xytext=(2, 2), + textcoords="offset points", + color="#FE9100", + fontsize=14, + ) + + # Configure plot appearance + plt.xlabel("Anterior →", fontsize=14) + plt.ylabel("Superior →", fontsize=14) + plt.margins(0) + + # Save or display the plot + plt.savefig(fig_path, bbox_inches="tight", dpi=300) + print(f"Plot saved to: {fig_path}") + plt.close() + + +def plot_sagittal_slice_with_landmarks_batch( + image_dir: str, landmark_dir: str, fig_dir: str +): + """Plot all cases from given directories. + + Args: + image_dir (str): Directory containing .nii.gz files + landmark_dir (str): Directory containing landmark JSON files + fig_dir (str): Directory to save output figures + + """ + # Create output directory if it doesn't exist + os.makedirs(fig_dir, exist_ok=True) + + # Process each .nii.gz file + for nii_path in glob.glob(os.path.join(image_dir, "*.nii.gz")): + base_name = os.path.splitext(os.path.splitext(os.path.basename(nii_path))[0])[0] + json_path = os.path.join(landmark_dir, f"{base_name}.json.gz") + fig_path = os.path.join(fig_dir, f"{base_name}.png") + + # Plot and save + if os.path.exists(json_path): + plot_sagittal_slice_with_landmarks(nii_path, json_path, fig_path) + else: + print(f"Warning: No landmark file found for {base_name}") + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download the file using urllib + url = "https://figshare.com/ndownloader/articles/3471833?private_link=37ec464af8e81ae6ebbf" + output_file = "Cephalogram400.zip" + print(f"Downloading file from {url}...") + urllib.request.urlretrieve(url, output_file) + + # Extract the ZIP file + print("Extracting ZIP file...") + with zipfile.ZipFile(output_file, "r") as zip_ref: + zip_ref.extractall() + + # Find and extract all RAR files + print("Extracting RAR files...") + for file in os.listdir("."): + if file.endswith(".rar"): + with rarfile.RarFile(file) as rf: + rf.extractall() + + # Create the Images-raw directory + os.makedirs("Images-raw", exist_ok=True) + # Move all BMP files from RawImage to Images-raw using glob + for src_path in glob.glob(os.path.join("RawImage", "**", "*.bmp"), recursive=True): + shutil.move(src_path, os.path.join("Images-raw", os.path.basename(src_path))) + + # Convert BMP files to 3D nii.gz + Flag_flip_dim0 = True + Flag_flip_dim1 = False + Flag_swap_dim01 = True + img_size_dim0, img_size_dim1 = convert_bmp_to_niigz( + "Images-raw", + "Images", + slice_dim_type=0, + pseudo_voxel_size=[0.1, 0.1, 0.1], + flip_dim0=Flag_flip_dim0, + flip_dim1=Flag_flip_dim1, + swap_dim01=Flag_swap_dim01, + ) + + # Read landmark points from txt files and save as JSON + process_landmarks_data( + "400_senior", + "Landmarks", + 19, + img_sizes=[img_size_dim0, img_size_dim1], + flip_dim0=Flag_flip_dim0, + flip_dim1=Flag_flip_dim1, + swap_dim01=Flag_swap_dim01, + ) + + # Plot slices with landmarks + plot_sagittal_slice_with_landmarks_batch("Images", "Landmarks", "Landmarks-fig") + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Landmarks", + "Landmarks-fig", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/Ceph_Biometrics_400/preprocess_biometry.py b/src/medvision_ds/datasets/Ceph_Biometrics_400/preprocess_biometry.py new file mode 100644 index 0000000000000000000000000000000000000000..51aab6f5bdd4f4ef01ad003effa26419424b7512 --- /dev/null +++ b/src/medvision_ds/datasets/Ceph_Biometrics_400/preprocess_biometry.py @@ -0,0 +1,373 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerBiometry + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# Do not change the dictionray names +# - dataset_info, landmarks_map, lines_map, angles_map, biometrics_map +# ==================================== +dataset_info = { + "dataset": "Ceph-Biometrics-400", + "dataset_website": "", + "dataset_data": [ + "https://figshare.com/s/37ec464af8e81ae6ebbf", + "https://huggingface.co/datasets/YongchengYAO/Ceph-Biometrics-400", + ], + "license": ["N/A", "CC BY-NC 4.0"], + "paper": ["https://doi.org/10.1038/srep33581"], +} + +landmarks_map = { + "P1": "sella", + "P2": "nasion", + "P3": "orbitale", + "P4": "porion", + "P5": "subspinale", + "P6": "supramentale", + "P7": "pogonion", + "P8": "menton", + "P9": "gnathion", + "P10": "gonion", + "P11": "incision inferius", + "P12": "incision superius", + "P13": "upper lip", + "P14": "lower lip", + "P15": "subnasale", + "P16": "soft tissue pogonion", + "P17": "posterior nasal spine", + "P18": "anterior nasal spine", + "P19": "articulare", +} + +lines_map = { + "L-1-2": { + "name": "", + "element_keys": ["P1", "P2"], + "element_map_name": "landmarks_map", + }, + "L-1-10": { + "name": "", + "element_keys": ["P1", "P10"], + "element_map_name": "landmarks_map", + }, + "L-2-5": { + "name": "", + "element_keys": ["P2", "P5"], + "element_map_name": "landmarks_map", + }, + "L-2-6": { + "name": "", + "element_keys": ["P2", "P6"], + "element_map_name": "landmarks_map", + }, + "L-2-7": { + "name": "", + "element_keys": ["P2", "P7"], + "element_map_name": "landmarks_map", + }, + "L-2-8": { + "name": "", + "element_keys": ["P2", "P8"], + "element_map_name": "landmarks_map", + }, + "L-3-4": { + "name": "", + "element_keys": ["P3", "P4"], + "element_map_name": "landmarks_map", + }, + "L-5-6": { + "name": "", + "element_keys": ["P5", "P6"], + "element_map_name": "landmarks_map", + }, + "L-8-10": { + "name": "", + "element_keys": ["P8", "P10"], + "element_map_name": "landmarks_map", + }, + "L-9-10": { + "name": "", + "element_keys": ["P9", "P10"], + "element_map_name": "landmarks_map", + }, + "L-17-18": { + "name": "", + "element_keys": ["P17", "P18"], + "element_map_name": "landmarks_map", + }, +} + +angles_map = { + "A-L_2_5-L_2_6": { + "name": "", + "element_keys": ["L-2-5", "L-2-6"], + "element_map_name": "lines_map", + }, + "A-L_1_2-L_2_6": { + "name": "", + "element_keys": ["L-1-2", "L-2-6"], + "element_map_name": "lines_map", + }, + "A-L_1_2-L_2_5": { + "name": "", + "element_keys": ["L-1-2", "L-2-5"], + "element_map_name": "lines_map", + }, + "A-L_5_6-L_8_10": { + "name": "", + "element_keys": ["L-5-6", "L-8-10"], + "element_map_name": "lines_map", + }, + "A-L_3_4-L_2_7": { + "name": "", + "element_keys": ["L-3-4", "L-2-7"], + "element_map_name": "lines_map", + }, + "A-L_3_4-L_17_18": { + "name": "", + "element_keys": ["L-3-4", "L-17-18"], + "element_map_name": "lines_map", + }, + "A-L_2_7-L_5_6": { + "name": "", + "element_keys": ["L-2-7", "L-5-6"], + "element_map_name": "lines_map", + }, + "A-L_1_2-L_9_10": { + "name": "", + "element_keys": ["L-1-2", "L-9-10"], + "element_map_name": "lines_map", + }, +} + +biometrics_map = [ + { + "metric_type": "angle", + "metric_map_name": "angles_map", + "metric_key": "A-L_2_5-L_2_6", + "slice_dim": 0, + }, + { + "metric_type": "angle", + "metric_map_name": "angles_map", + "metric_key": "A-L_1_2-L_2_6", + "slice_dim": 0, + }, + { + "metric_type": "angle", + "metric_map_name": "angles_map", + "metric_key": "A-L_1_2-L_2_5", + "slice_dim": 0, + }, + { + "metric_type": "angle", + "metric_map_name": "angles_map", + "metric_key": "A-L_5_6-L_8_10", + "slice_dim": 0, + }, + { + "metric_type": "angle", + "metric_map_name": "angles_map", + "metric_key": "A-L_3_4-L_2_7", + "slice_dim": 0, + }, + { + "metric_type": "angle", + "metric_map_name": "angles_map", + "metric_key": "A-L_3_4-L_17_18", + "slice_dim": 0, + }, + { + "metric_type": "angle", + "metric_map_name": "angles_map", + "metric_key": "A-L_2_7-L_5_6", + "slice_dim": 0, + }, + { + "metric_type": "angle", + "metric_map_name": "angles_map", + "metric_key": "A-L_1_2-L_9_10", + "slice_dim": 0, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-1-2", + "slice_dim": 0, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-1-10", + "slice_dim": 0, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-2-5", + "slice_dim": 0, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-2-6", + "slice_dim": 0, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-2-7", + "slice_dim": 0, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-2-8", + "slice_dim": 0, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-3-4", + "slice_dim": 0, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-5-6", + "slice_dim": 0, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-8-10", + "slice_dim": 0, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-9-10", + "slice_dim": 0, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-17-18", + "slice_dim": 0, + }, +] + + +# ------------ +# Task-specific benchmark planning configuration +# ------------ +# - dataset_info: Dictionary containing dataset metadata +# - tasks: List of task configurations where each task contains: +# - image_modality: Type of medical imaging (e.g., "CT", "MRI") +# - image_description: Description of image, used in text prompts +# - image_folder: Directory for .nii.gz image files +# - landmark_folder: Directory for landmark files +# - image_prefix: Filename part before case ID for images +# - image_suffix: Filename part after case ID for images +# - landmark_prefix: Filename part before case ID for landmarks +# - landmark_suffix: Filename part after case ID for landmarks +# - landmarks_map: Dictionary mapping landmarks to their descriptions +# NOTE: +# - These keys should match the variable names: +# "landmarks_map": landmarks_map, +# "lines_map": lines_map, +# "angles_map": angles_map, +# "biometrics_map": biometrics_map, +# ------------ +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "X Ray", + "image_description": "cephalogram (head and neck X-ray)", + "image_folder": "Images", + "landmark_folder": "Landmarks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerBiometry( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for biometric measurement task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + ) diff --git a/src/medvision_ds/datasets/CrossMoDA/__init__.py b/src/medvision_ds/datasets/CrossMoDA/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/CrossMoDA/download_fast.py b/src/medvision_ds/datasets/CrossMoDA/download_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..38567874323e5c5fed56526f6dacb77fea3af6fd --- /dev/null +++ b/src/medvision_ds/datasets/CrossMoDA/download_fast.py @@ -0,0 +1,103 @@ +import os +import shutil +import argparse +import zipfile +from huggingface_hub import hf_hub_download +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# [CrossMoDA] +# Challenge: https://crossmoda-challenge.ml +# Data: https://zenodo.org/records/4662239 +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download and extract dataset + for file in ["Images.zip", "Masks.zip"]: + # Download and extract dataset + hf_hub_download( + repo_id="YongchengYAO/CrossMoDA-Lite", + filename=file, + repo_type="dataset", + revision="7e35dd56061d1e15e814276f8cdef4b42dcbe9fd", # commit hash on 2025-02-26 + local_dir=".", + ) + print(f"Extracting {file}... This may take some time.") + with zipfile.ZipFile(file, 'r') as zip_ref: + zip_ref.extractall() + os.remove(file) + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/CrossMoDA/download_raw.py b/src/medvision_ds/datasets/CrossMoDA/download_raw.py new file mode 100644 index 0000000000000000000000000000000000000000..277c203c56946d0d2a2a5369ba30bc4d26978607 --- /dev/null +++ b/src/medvision_ds/datasets/CrossMoDA/download_raw.py @@ -0,0 +1,113 @@ +import os +import shutil +import glob +import argparse +import zipfile +import urllib.request +from medvision_ds.utils.preprocess_utils import process_dataset, move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# [CrossMoDA] +# Challenge: https://crossmoda-challenge.ml +# Data: https://zenodo.org/records/4662239 +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download dataset from Zenodo + url = "https://zenodo.org/records/4662239/files/crossmoda_training.zip?download=1" + print(f"Downloading from {url}...") + urllib.request.urlretrieve(url, "crossmoda_training.zip") + + # Extract downloaded zip file + print("Extracting zip file...") + with zipfile.ZipFile("crossmoda_training.zip", "r") as zip_ref: + zip_ref.extractall("crossmoda_training") + + os.makedirs("Images", exist_ok=True) + os.makedirs("Masks", exist_ok=True) + # Move ceT1 files to Images folder + for file in glob.glob( + os.path.join("crossmoda_training", "source_training", "*_ceT1.nii.gz") + ): + shutil.move(file, os.path.join("Images", os.path.basename(file))) + + # Move Label files to Masks folder + for file in glob.glob( + os.path.join("crossmoda_training", "source_training", "*_Label.nii.gz") + ): + shutil.move(file, os.path.join("Masks", os.path.basename(file))) + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/CrossMoDA/preprocess_detection.py b/src/medvision_ds/datasets/CrossMoDA/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..67cb15c7749967d996bd7a5c3e6444cf032fb4dc --- /dev/null +++ b/src/medvision_ds/datasets/CrossMoDA/preprocess_detection.py @@ -0,0 +1,129 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "CrossMoDA", + "dataset_website": "https://crossmoda-challenge.ml", + "dataset_data": [ + "https://zenodo.org/records/4662239", + ], + "license": ["CC BY 4.0"], + "paper": ["https://doi.org/10.1016/j.media.2022.102628"], +} + +labels_map = { + "1": "vestibular schwannoma (acoustic neuroma)", + "2": "cochlea", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "contrast-enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_ceT1.nii.gz", + "mask_prefix": "", + "mask_suffix": "_Label.nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/CrossMoDA/preprocess_segmentation.py b/src/medvision_ds/datasets/CrossMoDA/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..8647a71a3d828eed5a7911d2eae84e856791e2df --- /dev/null +++ b/src/medvision_ds/datasets/CrossMoDA/preprocess_segmentation.py @@ -0,0 +1,129 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "CrossMoDA", + "dataset_website": "https://crossmoda-challenge.ml", + "dataset_data": [ + "https://zenodo.org/records/4662239", + ], + "license": ["CC BY 4.0"], + "paper": ["https://doi.org/10.1016/j.media.2022.102628"], +} + +labels_map = { + "1": "vestibular schwannoma", + "2": "cochlea", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "contrast-enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_ceT1.nii.gz", + "mask_prefix": "", + "mask_suffix": "_Label.nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/FLARE22/__init__.py b/src/medvision_ds/datasets/FLARE22/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/FLARE22/download_fast.py b/src/medvision_ds/datasets/FLARE22/download_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..f8284283e5238d4fb60adef9d01dab0c0776529b --- /dev/null +++ b/src/medvision_ds/datasets/FLARE22/download_fast.py @@ -0,0 +1,103 @@ +import os +import shutil +import argparse +import zipfile +from huggingface_hub import hf_hub_download +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: FLARE22 +# Challenge: https://flare22.grand-challenge.org +# Data: https://drive.google.com/drive/folders/1x0l-bxte46QFn5K_ZJzBxp8HsscF8v6t +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download and extract dataset + for file in ["Images.zip", "Masks.zip"]: + # Download and extract dataset + hf_hub_download( + repo_id="YongchengYAO/FLARE22-Lite", + filename=file, + repo_type="dataset", + revision="7c6db3f040fd9f9849e236651c828a22ef0f4f2f", # commit hash on 2025-02-21 + local_dir=".", + ) + print(f"Extracting {file}... This may take some time.") + with zipfile.ZipFile(file, "r") as zip_ref: + zip_ref.extractall() + os.remove(file) + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/FLARE22/download_raw.py b/src/medvision_ds/datasets/FLARE22/download_raw.py new file mode 100644 index 0000000000000000000000000000000000000000..a95a16741b51ea5d3587dfc2457f3a0fe98b7005 --- /dev/null +++ b/src/medvision_ds/datasets/FLARE22/download_raw.py @@ -0,0 +1,105 @@ +import os +import shutil +import argparse +import gdown +import zipfile +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: FLARE22 +# Challenge: https://flare22.grand-challenge.org +# Data: https://drive.google.com/drive/folders/1x0l-bxte46QFn5K_ZJzBxp8HsscF8v6t +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download folder from Google Drive + gdown.download_folder( + "https://drive.google.com/drive/folders/130DVWqCFALnpHIqnT6aucNIwYygemNjV?usp=share_link" + ) + + # Extract images + with zipfile.ZipFile( + os.path.join("FLARE22_LabeledCase50", "images.zip"), "r" + ) as zip_ref: + zip_ref.extractall("Images") + + # Extract masks + with zipfile.ZipFile( + os.path.join("FLARE22_LabeledCase50", "labels.zip"), "r" + ) as zip_ref: + zip_ref.extractall("Masks") + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/FLARE22/preprocess_detection.py b/src/medvision_ds/datasets/FLARE22/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..dab047e85dd85e40b3826f9cd54c7def1493fb89 --- /dev/null +++ b/src/medvision_ds/datasets/FLARE22/preprocess_detection.py @@ -0,0 +1,140 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "FLARE22", + "dataset_website": "https://flare22.grand-challenge.org", + "dataset_data": [ + "https://drive.google.com/drive/folders/1x0l-bxte46QFn5K_ZJzBxp8HsscF8v6t", + ], + "license": [""], + "paper": ["https://doi.org/10.1016/S2589-7500(24)00154-7"], +} + +labels_map = { + "1": "liver", + "2": "right kidney", + "3": "spleen", + "4": "pancreas", + "5": "aorta", + "6": "inferior vena cava (ivc)", + "7": "right adrenal gland (rag)", + "8": "left adrenal gland (lag)", + "9": "gallbladder", + "10": "esophagus", + "11": "stomach", + "12": "duodenum", + "13": "left kidney", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/FLARE22/preprocess_segmentation.py b/src/medvision_ds/datasets/FLARE22/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..53222c9b1ef5a1d39c9aa29544dab989a6691224 --- /dev/null +++ b/src/medvision_ds/datasets/FLARE22/preprocess_segmentation.py @@ -0,0 +1,140 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "FLARE22", + "dataset_website": "https://flare22.grand-challenge.org", + "dataset_data": [ + "https://drive.google.com/drive/folders/1x0l-bxte46QFn5K_ZJzBxp8HsscF8v6t", + ], + "license": [""], + "paper": ["https://doi.org/10.1016/S2589-7500(24)00154-7"], +} + +labels_map = { + "1": "liver", + "2": "right kidney", + "3": "spleen", + "4": "pancreas", + "5": "aorta", + "6": "inferior vena cava (ivc)", + "7": "right adrenal gland (rag)", + "8": "left adrenal gland (lag)", + "9": "gallbladder", + "10": "esophagus", + "11": "stomach", + "12": "duodenum", + "13": "left kidney", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/FeTA24/__init__.py b/src/medvision_ds/datasets/FeTA24/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/FeTA24/download.py b/src/medvision_ds/datasets/FeTA24/download.py new file mode 100644 index 0000000000000000000000000000000000000000..77d514db910693676689fe6f388cbb21359f72ae --- /dev/null +++ b/src/medvision_ds/datasets/FeTA24/download.py @@ -0,0 +1,459 @@ +import os +import shutil +import argparse +import synapseclient +import glob +import json +import zipfile +import gzip +import nibabel as nib +import numpy as np +import matplotlib.pyplot as plt +import SimpleITK as sitk +from pathlib import Path +from medvision_ds.utils.preprocess_utils import process_dataset, move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: FeTA24 +# Challenge: https://fetachallenge.github.io +# Data: (only 80/120 cases are available after the FeTA24 challenge) +# - Data from the University Children’s Hospital Zurich (80 cases): +# https://www.synapse.org/Synapse:syn25649159/wiki/610007 +# Format: nii.gz +# ==================================== + + +# Map of landmark points to their corresponding slice dimension +LANDMARKS_SLICE_DIM = { + "P1": 0, + "P2": 0, + "P3": 0, + "P4": 0, + "P5": 2, + "P6": 2, + "P7": 2, + "P8": 2, + "P9": 1, + "P10": 1, +} + +LABELS_NAME = { + "1": "Corpus_Callosum_Length", + "2": "Vermis_Height", + "3": "Brain_Biparietal_Diameter", + "4": "Skull_Biparietal_Diameter", + "5": "Transverse_Cerebellar_Diameter", +} + + +def im_original_to_realigned(feta_dir, biometry_dir, out_dir, seg=False): + """ + Transform images from original space to realigned space. + + Args: + feta_dir (str): Path to the original FeTA images directory + biometry_dir (str): Path to the biometric measurements directory + out_dir (str): Path to the output directory + seg (bool): If True, process segmentations instead of T2w images + """ + # Convert paths to absolute paths + feta_dir = os.path.abspath(feta_dir) + biometry_dir = os.path.abspath(biometry_dir) + out_dir = os.path.abspath(out_dir) + + # Validate input directories + if not os.path.exists(feta_dir): + raise FileNotFoundError(f"Folder {feta_dir} does not exist.") + if not os.path.exists(biometry_dir): + raise FileNotFoundError(f"Folder {biometry_dir} does not exist.") + + # Create output directory + os.makedirs(out_dir, exist_ok=True) + + # Get list of subjects (removing 'sub-' prefix) + sub_list = sorted([f[4:] for f in os.listdir(feta_dir) if "sub" in f]) + + # Initialize counter for saved files + files_saved = 0 + + # Process each subject + for sub in sub_list: + # Construct paths for current subject using os.path.join for cross-platform compatibility + sub_path_feta = os.path.join(feta_dir, f"sub-{sub}", "anat") + sub_path_bio = os.path.join(biometry_dir, f"sub-{sub}", "anat") + + # Skip if no biometry data exists + if not os.path.exists(sub_path_bio): + continue + + # Determine file suffix based on processing mode + suffix = "dseg.nii.gz" if seg else "T2w.nii.gz" + + # Read input image and transformation + imp = get_file(sub_path_feta, suffix=suffix) + im = sitk.ReadImage(imp) + trf = sitk.ReadTransform(get_file(sub_path_bio, suffix=".txt")) + + # Set up output path + out_path = os.path.join(out_dir, f"sub-{sub}", "anat", os.path.basename(imp)) + os.makedirs(os.path.dirname(out_path), exist_ok=True) + + # Choose interpolation method based on image type + interMethod = sitk.sitkNearestNeighbor if seg else sitk.sitkLinear + + # Apply transformation and save + im = sitk.Resample(im, im, trf, interMethod) + sitk.WriteImage(im, out_path) + files_saved += 1 + + print(f"-- Total files saved: {files_saved}") + + +def get_file(folder, suffix): + for file in os.listdir(folder): + if file.endswith(suffix): + return os.path.join(folder, file) + return None + + +def process_landmark_points( + landmark_mask_dir: str, + landmark_json_dir: str, + img_dir: str, + fig_dir: str, + slice_dim_map: dict = LANDMARKS_SLICE_DIM, + labels_name: dict = LABELS_NAME, +): + """Process landmark points from mask files and save coordinates and visualizations.""" + + # Create output directories + for dir_path in [landmark_json_dir, img_dir, fig_dir]: + os.makedirs(dir_path, exist_ok=True) + + def plot_landmarks(img_data, points, point_names, slice_dim, coord, save_path): + """Helper function to plot landmarks on image slices.""" + plt.figure() + + # Handle different slice orientations + if slice_dim == 0: # Sagittal + plt.imshow(img_data[coord, :, :].T, cmap="gray", origin="lower") + x_coords, y_coords = points[:, 1], points[:, 2] + plt.xlabel("Anterior →", fontsize=14) + plt.ylabel("Superior →", fontsize=14) + elif slice_dim == 1: # Coronal + plt.imshow(img_data[:, coord, :].T, cmap="gray", origin="lower") + x_coords, y_coords = points[:, 0], points[:, 2] + plt.xlabel("Right →", fontsize=14) + plt.ylabel("Superior →", fontsize=14) + else: # Axial + plt.imshow(img_data[:, :, coord].T, cmap="gray", origin="lower") + x_coords, y_coords = points[:, 0], points[:, 1] + plt.xlabel("Right →", fontsize=14) + plt.ylabel("Anterior →", fontsize=14) + + # Plot points and labels + for i, (x, y) in enumerate(zip(x_coords, y_coords)): + plt.scatter( + [x], + [y], + facecolors="#18A727", + edgecolors="black", + marker="o", + s=80, + linewidth=1.5, + label=point_names[i], + ) + plt.annotate( + point_names[i], + (x, y), + xytext=(2, 2), + textcoords="offset points", + color="#FE9100", + fontsize=14, + ) + + plt.margins(0) + plt.savefig(save_path) + plt.close() + + for nii_file in glob.glob(os.path.join(landmark_mask_dir, "*.nii.gz")): + try: + nii_path = Path(nii_file) + data = nib.load(str(nii_path)).get_fdata() + img_data = nib.load( + os.path.join( + img_dir, nii_path.name.replace("meas.nii.gz", "T2w.nii.gz") + ) + ).get_fdata() + + # This data structure is designed to be compatible with biometric data constructed from segmentation masks + json_dict = { + "slice_landmarks_x": [], + "slice_landmarks_y": [], + "slice_landmarks_z": [], + } + + # Process each label + for label in range(1, 6): + # Get coordinates + coords = np.where(data == label) + points = np.array(list(zip(coords[0], coords[1], coords[2]))) + + if len(points) != 2: + raise ValueError( + f"Label {label} has {len(points)} points, expected 2" + ) + + # Determine point indices and names based on label + subfolder = os.path.join(fig_dir, labels_name[str(label)]) + os.makedirs(subfolder, exist_ok=True) + + # Determine point order based on anatomical measurement type + if label == 1: # Corpus Callosum Length + idx_larger = np.argmax(points[:, 1]) # More anterior point + point_names = ["P1", "P2"] + elif label == 2: # Vermis Height + idx_larger = np.argmax(points[:, 2]) # Superior point + point_names = ["P3", "P4"] + elif label == 3: # Brain Biparietal Diameter + idx_larger = np.argmax(points[:, 0]) # Right point + point_names = ["P5", "P6"] + elif label == 4: # Skull Biparietal Diameter + idx_larger = np.argmax(points[:, 0]) # Right point + point_names = ["P7", "P8"] + else: # Transverse Cerebellar Diameter + idx_larger = np.argmax(points[:, 0]) # Right point + point_names = ["P9", "P10"] + + idx_smaller = 1 - idx_larger + sorted_points = points[[idx_larger, idx_smaller]] + + # Save landmark coordinates + slice_dim = slice_dim_map[point_names[0]] + if slice_dim == 0: + json_dict_key = "slice_landmarks_x" + elif slice_dim == 1: + json_dict_key = "slice_landmarks_y" + else: + json_dict_key = "slice_landmarks_z" + slice_idx = sorted_points[0].tolist()[slice_dim] + landmarks_dict = { + "slice_idx": slice_idx, + "landmarks": { + point_names[0]: sorted_points[0].tolist(), + point_names[1]: sorted_points[1].tolist(), + }, + } + json_dict[json_dict_key].append(landmarks_dict) + + # Plot visualization + slice_dim = slice_dim_map[point_names[0]] + for coord in sorted_points[:, slice_dim].astype(int): + save_path = os.path.join( + subfolder, + f"{nii_path.name.replace('_meas.nii.gz','')}_slice{coord}.png", + ) + plot_landmarks( + img_data, + sorted_points, + point_names, + slice_dim, + coord, + save_path, + ) + + # Save landmarks to JSON + with gzip.open( + os.path.join( + landmark_json_dir, + f"{nii_path.name.replace('_meas.nii.gz','')}.json.gz", + ), + "wt", + ) as f: + json.dump(json_dict, f, indent=4) + print(f"Processed {nii_path.name}") + + except Exception as e: + print(f"Error processing {nii_path.name}: {str(e)}") + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Initialize Synapse client + syn = synapseclient.Synapse() + token = os.environ.get("SYNAPSE_TOKEN") + if not token: + raise ValueError("SYNAPSE_TOKEN environment variable not set") + syn.login(authToken=token) + + # Download dataset using synapse client + syn.get("syn25649833", downloadLocation=tmp_dir) + + # Extract the downloaded zip file + with zipfile.ZipFile("feta_2.4.zip", "r") as zip_ref: + zip_ref.extractall(tmp_dir) + + print("Creating directories...") + dirs_to_create = [ + "Images", + "Images-reoriented", + "Masks", + "Masks-reoriented", + "Masks-landmarks-reoriented", + ] + for dir_name in dirs_to_create: + os.makedirs(os.path.join(tmp_dir, dir_name), exist_ok=True) + + # Set up paths + dir_feta = os.path.join(tmp_dir, "feta_2.4") + dir_biometry = os.path.join(tmp_dir, "feta_2.4", "derivatives", "biometry") + dir_reo = os.path.join(tmp_dir, "feta_2.4", "derivatives", "im_reo") + + print("Reorient images to biometry measurement space...") + im_original_to_realigned( + feta_dir=dir_feta, biometry_dir=dir_biometry, out_dir=dir_reo, seg=False + ) + + print("Reorient masks to biometry measurement space...") + im_original_to_realigned( + feta_dir=dir_feta, biometry_dir=dir_biometry, out_dir=dir_reo, seg=True + ) + + print("Moving reoriented images and masks...") + process_dataset( + [str(dir_reo)], + "*_dseg.nii.gz", + "_dseg.nii.gz", + img_suffix="_T2w.nii.gz", + out_dir=tmp_dir, + images_fname="Images-reoriented", + masks_fname="Masks-reoriented", + ) + + print("Moving landmark files...") + landmark_count = 0 + for lm_file in glob.glob( + os.path.join(dir_biometry, "**", "*meas.nii.gz"), recursive=True + ): + shutil.move( + lm_file, + os.path.join( + tmp_dir, "Masks-landmarks-reoriented", os.path.basename(lm_file) + ), + ) + landmark_count += 1 + print(f"-- Moved {landmark_count} landmark files") + + print("Moving raw images and masks...") + process_dataset( + ["feta_2.4"], + "*_dseg.nii.gz", + "_dseg.nii.gz", + img_suffix="_T2w.nii.gz", + out_dir=tmp_dir, + ) + + # List of cases to remove (base filenames without extension) + cases_to_remove = [ + "sub-005_rec-mial", + "sub-025_rec-mial", + "sub-066_rec-irtk", + "sub-032_rec-mial", + "sub-016_rec-mial", + ] + + # Remove landmarks and images for each case + for case in cases_to_remove: + for path in [ + os.path.join(tmp_dir, "Masks-landmarks-reoriented", f"{case}_meas.nii.gz"), + os.path.join(tmp_dir, "Images-reoriented", f"{case}_T2w.nii.gz"), + ]: + if os.path.exists(path): + os.remove(path) + print(f"Removed {os.path.basename(path)}") + + # Save landmark coordinates to json file + process_landmark_points( + landmark_mask_dir="Masks-landmarks-reoriented", + landmark_json_dir="Landmarks", + img_dir="Images-reoriented", + fig_dir="Landmarks-fig", + slice_dim_map=LANDMARKS_SLICE_DIM, + labels_name=LABELS_NAME, + ) + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Images-reoriented", + "Landmarks", + "Landmarks-fig", + "Masks", + "Masks-reoriented", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/FeTA24/preprocess_biometry.py b/src/medvision_ds/datasets/FeTA24/preprocess_biometry.py new file mode 100644 index 0000000000000000000000000000000000000000..fccdc6fb8d64fe47b458e8660db328405bb6f983 --- /dev/null +++ b/src/medvision_ds/datasets/FeTA24/preprocess_biometry.py @@ -0,0 +1,207 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerBiometry + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "FeTA24", + "dataset_website": "https://fetachallenge.github.io", + "dataset_data": [ + "https://www.synapse.org/Synapse:syn25649159/wiki/610007", + ], + "license": [""], + "paper": ["https://zenodo.org/records/10986046"], +} + +landmarks_map = { + "P1": "most anterior point of corpus callosum", + "P2": "most posterior point of corpus callosum", + "P3": "most superior point of vermis", + "P4": "most inferior point of vermis", + "P5": "right parietal eminence ", + "P6": "left parietal eminence", + "P7": "right skull parietal eminence", + "P8": "left skull parietal eminence", + "P9": "most right point of cerebellar hemisphere", + "P10": "most left point of cerebellar hemisphere", +} + +lines_map = { + "L-1-2": { + "name": "length of corpus callosum", + "element_keys": ["P1", "P2"], + "element_map_name": "landmarks_map", + }, + "L-3-4": { + "name": "height of vermis", + "element_keys": ["P3", "P4"], + "element_map_name": "landmarks_map", + }, + "L-5-6": { + "name": "brain biparietal diameter", + "element_keys": ["P5", "P6"], + "element_map_name": "landmarks_map", + }, + "L-7-8": { + "name": "skull biparietal diameter", + "element_keys": ["P7", "P8"], + "element_map_name": "landmarks_map", + }, + "L-9-10": { + "name": "transverse cerebellar diameter", + "element_keys": ["P9", "P10"], + "element_map_name": "landmarks_map", + }, +} + +angles_map = {} + +biometrics_map = [ + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-1-2", + "slice_dim": 0, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-3-4", + "slice_dim": 0, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-5-6", + "slice_dim": 2, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-7-8", + "slice_dim": 2, + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-9-10", + "slice_dim": 1, + }, +] + + +# ------------ +# Task-specific benchmark planning configuration +# ------------ +# - dataset_info: Dictionary containing dataset metadata +# - tasks: List of task configurations where each task contains: +# - image_modality: Type of medical imaging (e.g., "CT", "MRI") +# - image_description: Description of image, used in text prompts +# - image_folder: Directory for .nii.gz image files +# - landmark_folder: Directory for landmark files +# - image_prefix: Filename part before case ID for images +# - image_suffix: Filename part after case ID for images +# - landmark_prefix: Filename part before case ID for landmarks +# - landmark_suffix: Filename part after case ID for landmarks +# - landmarks_map: Dictionary mapping landmarks to their descriptions +# NOTE: +# - These keys should match the variable names: +# "landmarks_map": landmarks_map, +# "lines_map": lines_map, +# "angles_map": angles_map, +# "biometrics_map": biometrics_map, +# ------------ +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "", + "image_description": "", + "image_folder": "Images-reoriented", + "landmark_folder": "Landmarks", + "image_prefix": "", + "image_suffix": "_T2w.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerBiometry( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for biometric measurement task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + ) diff --git a/src/medvision_ds/datasets/FeTA24/preprocess_detection.py b/src/medvision_ds/datasets/FeTA24/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..ad82dc0a25cc821ddeda811a8cd758cab3fa3e2a --- /dev/null +++ b/src/medvision_ds/datasets/FeTA24/preprocess_detection.py @@ -0,0 +1,134 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "FeTA24", + "dataset_website": "https://fetachallenge.github.io", + "dataset_data": [ + "https://www.synapse.org/Synapse:syn25649159/wiki/610007", + ], + "license": [""], + "paper": ["https://zenodo.org/records/10986046"], +} + +labels_map = { + "1": "external cerebrospinal fluid", + "2": "grey matter", + "3": "white matter", + "4": "ventricles", + "5": "cerebellum", + "6": "deep grey matter", + "7": "brainstem", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "T2-weighted fetal brain magnetic resonance imaging (MRI) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_T2w.nii.gz", + "mask_prefix": "", + "mask_suffix": "_dseg.nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/FeTA24/preprocess_segmentation.py b/src/medvision_ds/datasets/FeTA24/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..aeceba951dbb490073bad6f86603e6f708e8cbb8 --- /dev/null +++ b/src/medvision_ds/datasets/FeTA24/preprocess_segmentation.py @@ -0,0 +1,134 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "FeTA24", + "dataset_website": "https://fetachallenge.github.io", + "dataset_data": [ + "https://www.synapse.org/Synapse:syn25649159/wiki/610007", + ], + "license": [""], + "paper": ["https://zenodo.org/records/10986046"], +} + +labels_map = { + "1": "external cerebrospinal fluid", + "2": "grey matter", + "3": "white matter", + "4": "ventricles", + "5": "cerebellum", + "6": "deep grey matter", + "7": "brainstem", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "T2-weighted fetal brain magnetic resonance imaging (MRI) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_T2w.nii.gz", + "mask_prefix": "", + "mask_suffix": "_dseg.nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/HNTSMRG24/__init__.py b/src/medvision_ds/datasets/HNTSMRG24/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/HNTSMRG24/download.py b/src/medvision_ds/datasets/HNTSMRG24/download.py new file mode 100644 index 0000000000000000000000000000000000000000..adb6a7c0bb35b4d82dba5fae372e1128fee7b435 --- /dev/null +++ b/src/medvision_ds/datasets/HNTSMRG24/download.py @@ -0,0 +1,123 @@ +import os +import shutil +import glob +import argparse +import zipfile +import urllib.request +from medvision_ds.utils.preprocess_utils import process_dataset, move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# [HNTSMRG24] +# Challenge: https://hntsmrg24.grand-challenge.org +# Data: https://zenodo.org/records/11199559 +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download dataset from Zenodo + print("Downloading dataset from Zenodo...") + url = "https://zenodo.org/records/11199559/files/HNTSMRG24_train.zip?download=1" + zip_path = "HNTSMRG24_train.zip" + urllib.request.urlretrieve(url, zip_path) + + # Extract downloaded zip file + print("Extracting zip file...") + with zipfile.ZipFile(zip_path, "r") as zip_ref: + zip_ref.extractall() + + # Create directory structure for both timepoints + for timepoint in ["HNTSMRG24-midRT", "HNTSMRG24-preRT"]: + os.makedirs(os.path.join(timepoint, "Images"), exist_ok=True) + os.makedirs(os.path.join(timepoint, "Masks"), exist_ok=True) + + # Process dataset for pre-RT timepoint + process_dataset( + data_dirs=["HNTSMRG24_train"], + seg_pattern="*_preRT_mask.nii.gz", + base_suffix="_mask.nii.gz", + img_suffix="_T2.nii.gz", + out_dir="HNTSMRG24-preRT", + ) + + # Process dataset for mid-RT timepoint + process_dataset( + data_dirs=["HNTSMRG24_train"], + seg_pattern="*_midRT_mask.nii.gz", + base_suffix="_mask.nii.gz", + img_suffix="_T2.nii.gz", + out_dir="HNTSMRG24-midRT", + ) + + # Move folder to dataset_dir + folders_to_move = [ + "HNTSMRG24-midRT", + "HNTSMRG24-preRT", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/HNTSMRG24/preprocess_biometry.py b/src/medvision_ds/datasets/HNTSMRG24/preprocess_biometry.py new file mode 100644 index 0000000000000000000000000000000000000000..849a98acb0a43600302b0f37b4278c50e1d72758 --- /dev/null +++ b/src/medvision_ds/datasets/HNTSMRG24/preprocess_biometry.py @@ -0,0 +1,264 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerBiometry_fromSeg + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +CLUSTER_SIZE_THRESHOLD = 200 + +dataset_info = { + "dataset": "HNTSMRG24", + "dataset_website": "https://hntsmrg24.grand-challenge.org", + "dataset_data": [ + "https://zenodo.org/records/11199559", + ], + "license": ["CC BY-NC 4.0"], + "paper": ["https://doi.org/10.48550/arXiv.2411.18585"], +} + +labels_map = { + "1": "primary gross tumor volume (head & neck)", + "2": "metastatic lymph node", +} + +landmarks_map = { + "P1": "most right/anterior/superior endpoint of the major axis", + "P2": "most left/superior/inferior endpoint of the major axis", + "P3": "most right/anterior/superior endpoint of the minor axis", + "P4": "most left/superior/inferior endpoint of the minor axis", +} + +lines_map = { + "L-1-2": { + "name": "marjor axis of the fitted ellipse", + "element_keys": ["P1", "P2"], + "element_map_name": "landmarks_map", + }, + "L-3-4": { + "name": "minor axis of the fitted ellipse", + "element_keys": ["P3", "P4"], + "element_map_name": "landmarks_map", + }, +} + +angles_map = {} + +biometrics_map = [ + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-1-2", + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-3-4", + }, +] + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "T2-weighted head and neck magnetic resonance imaging (MRI) scan", + "image_folder": "HNTSMRG24-midRT/Images", + "mask_folder": "HNTSMRG24-midRT/Masks", + "landmark_folder": "HNTSMRG24-midRT/Landmarks-Label1", + "landmark_figure_folder": "HNTSMRG24-midRT/Landmarks-Label1-fig", + "image_prefix": "", + "image_suffix": "_T2.nii.gz", + "mask_prefix": "", + "mask_suffix": "_mask.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 1, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "T2-weighted head and neck magnetic resonance imaging (MRI) scan", + "image_folder": "HNTSMRG24-midRT/Images", + "mask_folder": "HNTSMRG24-midRT/Masks", + "landmark_folder": "HNTSMRG24-midRT/Landmarks-Label2", + "landmark_figure_folder": "HNTSMRG24-midRT/Landmarks-Label2-fig", + "image_prefix": "", + "image_suffix": "_T2.nii.gz", + "mask_prefix": "", + "mask_suffix": "_mask.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 2, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "T2-weighted head and neck magnetic resonance imaging (MRI) scan", + "image_folder": "HNTSMRG24-preRT/Images", + "mask_folder": "HNTSMRG24-preRT/Masks", + "landmark_folder": "HNTSMRG24-preRT/Landmarks-Label1", + "landmark_figure_folder": "HNTSMRG24-preRT/Landmarks-Label1-fig", + "image_prefix": "", + "image_suffix": "_T2.nii.gz", + "mask_prefix": "", + "mask_suffix": "_mask.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 1, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "T2-weighted head and neck magnetic resonance imaging (MRI) scan", + "image_folder": "HNTSMRG24-preRT/Images", + "mask_folder": "HNTSMRG24-preRT/Masks", + "landmark_folder": "HNTSMRG24-preRT/Landmarks-Label2", + "landmark_figure_folder": "HNTSMRG24-preRT/Landmarks-Label2-fig", + "image_prefix": "", + "image_suffix": "_T2.nii.gz", + "mask_prefix": "", + "mask_suffix": "_mask.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 2, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + shrunken_bbox_scale=0.9, + enlarged_bbox_scale=1.1, + force_uint16_mask=False, + reorient2RAS=False, + visualization=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerBiometry_fromSeg( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + shrunk_bbox_scale=shrunken_bbox_scale, + enlarged_bbox_scale=enlarged_bbox_scale, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + visualization=visualization, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for biometric measurement task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--shrunken_bbox_scale", + type=float, + default=0.9, + help="Scale factor for shrunken bounding box", + ) + parser.add_argument( + "--enlarged_bbox_scale", + type=float, + default=1.1, + help="Scale factor for enlarged bounding box", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + parser.add_argument( + "--visualization", + action="store_true", + help="Enable visualization of the dataset processing", + ) + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + shrunken_bbox_scale=args.shrunken_bbox_scale, + enlarged_bbox_scale=args.enlarged_bbox_scale, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + visualization=args.visualization, + ) diff --git a/src/medvision_ds/datasets/HNTSMRG24/preprocess_detection.py b/src/medvision_ds/datasets/HNTSMRG24/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..3f09fcdc846421623bb3ede1bc4bde2b006039c1 --- /dev/null +++ b/src/medvision_ds/datasets/HNTSMRG24/preprocess_detection.py @@ -0,0 +1,140 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "HNTSMRG24", + "dataset_website": "https://hntsmrg24.grand-challenge.org", + "dataset_data": [ + "https://zenodo.org/records/11199559", + ], + "license": ["CC BY-NC 4.0"], + "paper": ["https://doi.org/10.48550/arXiv.2411.18585"], +} + +labels_map = { + "1": "primary gross tumor volume (head & neck)", + "2": "metastatic lymph node", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "T2-weighted head and neck magnetic resonance imaging (MRI) scan", + "image_folder": "HNTSMRG24-midRT/Images", + "mask_folder": "HNTSMRG24-midRT/Masks", + "image_prefix": "", + "image_suffix": "_T2.nii.gz", + "mask_prefix": "", + "mask_suffix": "_mask.nii.gz", + "labels_map": labels_map, + }, + { + "image_modality": "MRI", + "image_description": "T2-weighted head and neck magnetic resonance imaging (MRI) scan", + "image_folder": "HNTSMRG24-preRT/Images", + "mask_folder": "HNTSMRG24-preRT/Masks", + "image_prefix": "", + "image_suffix": "_T2.nii.gz", + "mask_prefix": "", + "mask_suffix": "_mask.nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/HNTSMRG24/preprocess_segmentation.py b/src/medvision_ds/datasets/HNTSMRG24/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..3cb64b0e6795fc6cab277d44b46d8afff37f3d4f --- /dev/null +++ b/src/medvision_ds/datasets/HNTSMRG24/preprocess_segmentation.py @@ -0,0 +1,140 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "HNTSMRG24", + "dataset_website": "https://hntsmrg24.grand-challenge.org", + "dataset_data": [ + "https://zenodo.org/records/11199559", + ], + "license": ["CC BY-NC 4.0"], + "paper": ["https://doi.org/10.48550/arXiv.2411.18585"], +} + +labels_map = { + "1": "primary gross tumor volume (head & neck)", + "2": "metastatic lymph node", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "T2-weighted head and neck magnetic resonance imaging (MRI) scan", + "image_folder": "HNTSMRG24-midRT/Images", + "mask_folder": "HNTSMRG24-midRT/Masks", + "image_prefix": "", + "image_suffix": "_T2.nii.gz", + "mask_prefix": "", + "mask_suffix": "_mask.nii.gz", + "labels_map": labels_map, + }, + { + "image_modality": "MRI", + "image_description": "T2-weighted head and neck magnetic resonance imaging (MRI) scan", + "image_folder": "HNTSMRG24-preRT/Images", + "mask_folder": "HNTSMRG24-preRT/Masks", + "image_prefix": "", + "image_suffix": "_T2.nii.gz", + "mask_prefix": "", + "mask_suffix": "_mask.nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/ISLES24/__init__.py b/src/medvision_ds/datasets/ISLES24/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/ISLES24/download.py b/src/medvision_ds/datasets/ISLES24/download.py new file mode 100644 index 0000000000000000000000000000000000000000..0dda9e6c03eeb9665ed5a174d3e507c04686327f --- /dev/null +++ b/src/medvision_ds/datasets/ISLES24/download.py @@ -0,0 +1,106 @@ +import os +import shutil +import argparse +import zipfile +from huggingface_hub import hf_hub_download +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: ISLES24 +# Challenge: https://isles-24.grand-challenge.org +# Data: +# - Original: https://isles-24.grand-challenge.org/dataset/ +# - Huggingface Dataset: https://huggingface.co/datasets/YongchengYAO/ISLES24-MR-Lite +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # Download dataset from Hugging Face Hub + files_to_download = ["Images-DWI.zip", "Images-ADC.zip", "Masks.zip"] + for filename in files_to_download: + hf_hub_download( + repo_id="YongchengYAO/ISLES24-MR-Lite", + filename=filename, + repo_type="dataset", + revision="16bedc54a9c1e4c32672f7a6ffdc838a3a195946", # commit hash on 2025-07-07 + local_dir=".", + ) + + # Extract the downloaded zip files + for filename in files_to_download: + with zipfile.ZipFile(filename, "r") as zip_ref: + zip_ref.extractall() + + # Move folder to dataset_dir + folders_to_move = [ + "Images-DWI", + "Images-ADC", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/ISLES24/preprocess_detection.py b/src/medvision_ds/datasets/ISLES24/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..e8e759c4415c8c5ab6b917269f416e90ffff4d36 --- /dev/null +++ b/src/medvision_ds/datasets/ISLES24/preprocess_detection.py @@ -0,0 +1,141 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "ISLES24", + "dataset_website": "https://isles-24.grand-challenge.org", + "dataset_data": [ + "https://isles-24.grand-challenge.org/dataset/", + "https://huggingface.co/datasets/YongchengYAO/ISLES24-MR-Lite", + ], + "license": ["CC-BY-NC"], + "paper": [ + "https://doi.org/10.48550/arXiv.2408.11142", + "https://doi.org/10.48550/arXiv.2403.19425", + ], +} + +labels_map = {"1": "stroke infarct"} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "apparent diffusion coefficient map of brain magnetic resonance imaging (MRI) scan", + "image_folder": "Images-ADC", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_adc.nii.gz", + "mask_prefix": "", + "mask_suffix": "_lesion-msk.nii.gz", + "labels_map": labels_map, + }, + { + "image_modality": "MRI", + "image_description": "diffusion-weighted imaging of brain magnetic resonance imaging (MRI) scan", + "image_folder": "Images-DWI", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_dwi.nii.gz", + "mask_prefix": "", + "mask_suffix": "_lesion-msk.nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/ISLES24/preprocess_segmentation.py b/src/medvision_ds/datasets/ISLES24/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..51e74091d9257297da95bfce356193a6014ae003 --- /dev/null +++ b/src/medvision_ds/datasets/ISLES24/preprocess_segmentation.py @@ -0,0 +1,141 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "ISLES24", + "dataset_website": "https://isles-24.grand-challenge.org", + "dataset_data": [ + "https://isles-24.grand-challenge.org/dataset/", + "https://huggingface.co/datasets/YongchengYAO/ISLES24-MR-Lite", + ], + "license": ["CC-BY-NC"], + "paper": [ + "https://doi.org/10.48550/arXiv.2408.11142", + "https://doi.org/10.48550/arXiv.2403.19425", + ], +} + +labels_map = {"1": "stroke infarct"} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "apparent diffusion coefficient map of brain magnetic resonance imaging (MRI) scan", + "image_folder": "Images-ADC", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_adc.nii.gz", + "mask_prefix": "", + "mask_suffix": "_lesion-msk.nii.gz", + "labels_map": labels_map, + }, + { + "image_modality": "MRI", + "image_description": "diffusion-weighted imaging of brain magnetic resonance imaging (MRI) scan", + "image_folder": "Images-DWI", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_dwi.nii.gz", + "mask_prefix": "", + "mask_suffix": "_lesion-msk.nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/KiPA22/__init__.py b/src/medvision_ds/datasets/KiPA22/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/KiPA22/download.py b/src/medvision_ds/datasets/KiPA22/download.py new file mode 100644 index 0000000000000000000000000000000000000000..e1469a255d4f0d73b00209587c4a0a01c523d7ce --- /dev/null +++ b/src/medvision_ds/datasets/KiPA22/download.py @@ -0,0 +1,121 @@ +import os +import shutil +import argparse +import zipfile +from huggingface_hub import hf_hub_download +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: KiPA22 +# Challenge: https://kipa22.grand-challenge.org +# Data: https://kipa22.grand-challenge.org/dataset/ +# Format: nii.gz +# ==================================== + + +# Define HuggingFace dataset ID +BiometricVQA_KiPA22_HF_ID = os.environ.get( + "BiometricVQA_KiPA22_HF_ID", "YongchengYAO/KiPA22" +) + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download dataset from HuggingFace + if BiometricVQA_KiPA22_HF_ID == "YongchengYAO/KiPA22": + hf_hub_download( + repo_id=BiometricVQA_KiPA22_HF_ID, + filename="train.zip", + repo_type="dataset", + revision="5d5761d6e4a17e911eca558b251d2f3e4650b85e", # commit hash on 2025-02-07 + local_dir=".", + ) + else: + hf_hub_download( + repo_id=BiometricVQA_KiPA22_HF_ID, + filename="train.zip", + repo_type="dataset", + local_dir=".", + ) + + # Extract zip file + with zipfile.ZipFile("train.zip", "r") as zip_ref: + zip_ref.extractall() + + # Move directories to standard locations + shutil.rmtree("Images") if os.path.exists("Images") else None + shutil.rmtree("Masks") if os.path.exists("Masks") else None + shutil.move(os.path.join("train", "image"), "Images") + shutil.move(os.path.join("train", "label"), "Masks") + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/KiPA22/preprocess_biometry.py b/src/medvision_ds/datasets/KiPA22/preprocess_biometry.py new file mode 100644 index 0000000000000000000000000000000000000000..c07876bf8b5841c1eec22a551a5297d2bbf07da3 --- /dev/null +++ b/src/medvision_ds/datasets/KiPA22/preprocess_biometry.py @@ -0,0 +1,212 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerBiometry_fromSeg + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: KiPA22 +# Challenge: https://kipa22.grand-challenge.org +# Data: https://kipa22.grand-challenge.org/dataset/ +# Format: nii.gz +# ==================================== +CLUSTER_SIZE_THRESHOLD = 200 + +dataset_info = { + "dataset": "KiPA22", + "dataset_website": "https://kipa22.grand-challenge.org", + "dataset_data": [ + "https://kipa22.grand-challenge.org/dataset/", + ], + "license": ["CC BY NC ND"], + "paper": [ + "https://doi.org/10.1016/j.media.2021.102055", + "https://doi.org/10.1016/j.media.2020.101722", + "https://doi.org/10.1016/j.eururo.2010.11.037", + "https://doi.org/10.1016/j.eururo.2012.05.056", + ], +} + +labels_map = {"1": "renal vein", "2": "kidney", "3": "renal artery", "4": "tumor"} +# ==================================== + + +# =============== +# DO NOT CHANGE +# =============== +landmarks_map = { + "P1": "most right/anterior/superior endpoint of the major axis", + "P2": "most left/superior/inferior endpoint of the major axis", + "P3": "most right/anterior/superior endpoint of the minor axis", + "P4": "most left/superior/inferior endpoint of the minor axis", +} + +lines_map = { + "L-1-2": { + "name": "marjor axis of the fitted ellipse", + "element_keys": ["P1", "P2"], + "element_map_name": "landmarks_map", + }, + "L-3-4": { + "name": "minor axis of the fitted ellipse", + "element_keys": ["P3", "P4"], + "element_map_name": "landmarks_map", + }, +} + +angles_map = {} + +biometrics_map = [ + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-1-2", + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-3-4", + }, +] +# =============== + + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "kidney computed tomography (CT) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "landmark_folder": "Landmarks-Label4", + "landmark_figure_folder": "Landmarks-Label4-fig", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 4, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + shrunken_bbox_scale=0.9, + enlarged_bbox_scale=1.1, + force_uint16_mask=False, + reorient2RAS=False, + visualization=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerBiometry_fromSeg( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + shrunk_bbox_scale=shrunken_bbox_scale, + enlarged_bbox_scale=enlarged_bbox_scale, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + visualization=visualization, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for biometric measurement task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--shrunken_bbox_scale", + type=float, + default=0.9, + help="Scale factor for shrunken bounding box", + ) + parser.add_argument( + "--enlarged_bbox_scale", + type=float, + default=1.1, + help="Scale factor for enlarged bounding box", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + parser.add_argument( + "--visualization", + action="store_true", + help="Enable visualization of the dataset processing", + ) + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + shrunken_bbox_scale=args.shrunken_bbox_scale, + enlarged_bbox_scale=args.enlarged_bbox_scale, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + visualization=args.visualization, + ) diff --git a/src/medvision_ds/datasets/KiPA22/preprocess_detection.py b/src/medvision_ds/datasets/KiPA22/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..9e73b2016c792ca64392d061c0d62ef902ce4d16 --- /dev/null +++ b/src/medvision_ds/datasets/KiPA22/preprocess_detection.py @@ -0,0 +1,133 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: KiPA22 +# Challenge: https://kipa22.grand-challenge.org +# Data: https://kipa22.grand-challenge.org/dataset/ +# Format: nii.gz +# ==================================== +dataset_info = { + "dataset": "KiPA22", + "dataset_website": "https://kipa22.grand-challenge.org", + "dataset_data": [ + "https://kipa22.grand-challenge.org/dataset/", + ], + "license": ["CC BY NC ND"], + "paper": [ + "https://doi.org/10.1016/j.media.2021.102055", + "https://doi.org/10.1016/j.media.2020.101722", + "https://doi.org/10.1016/j.eururo.2010.11.037", + "https://doi.org/10.1016/j.eururo.2012.05.056", + ], +} + +labels_map = {"1": "renal vein", "2": "kidney", "3": "renal artery", "4": "tumor"} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "kidney computed tomography (CT) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/KiPA22/preprocess_segmentation.py b/src/medvision_ds/datasets/KiPA22/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..273b3fcf91ab77568221f5e3b61e0453c718f77d --- /dev/null +++ b/src/medvision_ds/datasets/KiPA22/preprocess_segmentation.py @@ -0,0 +1,133 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: KiPA22 +# Challenge: https://kipa22.grand-challenge.org +# Data: https://kipa22.grand-challenge.org/dataset/ +# Format: nii.gz +# ==================================== +dataset_info = { + "dataset": "KiPA22", + "dataset_website": "https://kipa22.grand-challenge.org", + "dataset_data": [ + "https://kipa22.grand-challenge.org/dataset/", + ], + "license": ["CC BY NC ND"], + "paper": [ + "https://doi.org/10.1016/j.media.2021.102055", + "https://doi.org/10.1016/j.media.2020.101722", + "https://doi.org/10.1016/j.eururo.2010.11.037", + "https://doi.org/10.1016/j.eururo.2012.05.056", + ], +} + +labels_map = {"1": "renal vein", "2": "kidney", "3": "renal artery", "4": "kidney tumor"} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "kidney computed tomography (CT) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/KiTS23/__init__.py b/src/medvision_ds/datasets/KiTS23/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/KiTS23/download_fast.py b/src/medvision_ds/datasets/KiTS23/download_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..19e6711dc96ac148bdef7f91afbf7a60416e0923 --- /dev/null +++ b/src/medvision_ds/datasets/KiTS23/download_fast.py @@ -0,0 +1,107 @@ +import os +import shutil +import argparse +import zipfile +from huggingface_hub import hf_hub_download +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: KiTS23 +# Challenge: https://kits-challenge.org/kits23/ +# Official Release: https://github.com/neheller/kits23#data-download +# HF Release: https://huggingface.co/datasets/YongchengYAO/KiTS23-Lite +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download CT dataset from Hugging Face Hub + hf_hub_download( + repo_id="YongchengYAO/KiTS23-Lite", + filename="KiTS23.zip", + repo_type="dataset", + revision="9680c15fcce821bbaff00f939e56a1e805267006", # commit hash on 2025-02-20 + local_dir=".", + ) + + # Unzip the downloaded file + print("Extracting KiTS23.zip... This may take some time.") + with zipfile.ZipFile("KiTS23.zip", 'r') as zip_ref: + zip_ref.extractall() + + # Move contents from KiTS23 folder to current directory + for item in os.listdir("KiTS23"): + shutil.move(os.path.join("KiTS23", item), ".") + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/KiTS23/download_raw.py b/src/medvision_ds/datasets/KiTS23/download_raw.py new file mode 100644 index 0000000000000000000000000000000000000000..e64fc8464c77bb7a677f3653fa63f34d61ef03f9 --- /dev/null +++ b/src/medvision_ds/datasets/KiTS23/download_raw.py @@ -0,0 +1,117 @@ +import os +import subprocess +import shutil +import argparse +import glob +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: KiTS23 +# Challenge: https://kits-challenge.org/kits23/ +# Official Release: https://github.com/neheller/kits23#data-download +# HF Release: https://huggingface.co/datasets/YongchengYAO/KiTS23-Lite +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Clone and setup KiTS23 + subprocess.run(["git", "clone", "https://github.com/neheller/kits23"]) + os.chdir("kits23") + subprocess.run(["pip3", "install", "-e", "."]) + subprocess.run(["kits23_download_data"]) + shutil.move("dataset", "..") + os.chdir("..") + + # Create output directories + os.makedirs("Images", exist_ok=True) + os.makedirs("Masks", exist_ok=True) + + # Process each directory in dataset folder + for dir_path in glob.glob(os.path.join("dataset", "*", "")): + if os.path.isdir(dir_path): + # Get directory name without trailing slash + id = os.path.basename(os.path.normpath(dir_path)) + + # Process imaging file + img_path = os.path.join(dir_path, "imaging.nii.gz") + if os.path.exists(img_path): + shutil.move(img_path, os.path.join("Images", f"{id}.nii.gz")) + + # Process segmentation file + seg_path = os.path.join(dir_path, "segmentation.nii.gz") + if os.path.exists(seg_path): + shutil.move(seg_path, os.path.join("Masks", f"{id}.nii.gz")) + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/KiTS23/preprocess_biometry.py b/src/medvision_ds/datasets/KiTS23/preprocess_biometry.py new file mode 100644 index 0000000000000000000000000000000000000000..39b43f976507d8f30beabd68e76fa418b667edce --- /dev/null +++ b/src/medvision_ds/datasets/KiTS23/preprocess_biometry.py @@ -0,0 +1,208 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerBiometry_fromSeg + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +CLUSTER_SIZE_THRESHOLD = 200 + +dataset_info = { + "dataset": "KiTS23", + "dataset_website": "https://kits-challenge.org/kits23/", + "dataset_data": [ + "https://github.com/neheller/kits23#data-download", + ], + "license": ["CC BY-NC-SA 4.0"], + "paper": ["https://doi.org/10.48550/arXiv.2307.01984"], +} + +labels_map = { + "1": "kidney", + "2": "kidney tumor", + "3": "kidney cyst", +} +# ==================================== + + +# =============== +# DO NOT CHANGE +# =============== +landmarks_map = { + "P1": "most right/anterior/superior endpoint of the major axis", + "P2": "most left/superior/inferior endpoint of the major axis", + "P3": "most right/anterior/superior endpoint of the minor axis", + "P4": "most left/superior/inferior endpoint of the minor axis", +} + +lines_map = { + "L-1-2": { + "name": "marjor axis of the fitted ellipse", + "element_keys": ["P1", "P2"], + "element_map_name": "landmarks_map", + }, + "L-3-4": { + "name": "minor axis of the fitted ellipse", + "element_keys": ["P3", "P4"], + "element_map_name": "landmarks_map", + }, +} + +angles_map = {} + +biometrics_map = [ + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-1-2", + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-3-4", + }, +] +# =============== + + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "corticomedullary-phase computed tomography (CT) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "landmark_folder": "Landmarks-Label2", + "landmark_figure_folder": "Landmarks-Label2-fig", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 2, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + shrunken_bbox_scale=0.9, + enlarged_bbox_scale=1.1, + force_uint16_mask=False, + reorient2RAS=False, + visualization=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerBiometry_fromSeg( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + shrunk_bbox_scale=shrunken_bbox_scale, + enlarged_bbox_scale=enlarged_bbox_scale, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + visualization=visualization, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for biometric measurement task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--shrunken_bbox_scale", + type=float, + default=0.9, + help="Scale factor for shrunken bounding box", + ) + parser.add_argument( + "--enlarged_bbox_scale", + type=float, + default=1.1, + help="Scale factor for enlarged bounding box", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + parser.add_argument( + "--visualization", + action="store_true", + help="Enable visualization of the dataset processing", + ) + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + shrunken_bbox_scale=args.shrunken_bbox_scale, + enlarged_bbox_scale=args.enlarged_bbox_scale, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + visualization=args.visualization, + ) diff --git a/src/medvision_ds/datasets/KiTS23/preprocess_detection.py b/src/medvision_ds/datasets/KiTS23/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..b6e45103480d0326b911b5b09420fdd71d747650 --- /dev/null +++ b/src/medvision_ds/datasets/KiTS23/preprocess_detection.py @@ -0,0 +1,130 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "KiTS23", + "dataset_website": "https://kits-challenge.org/kits23/", + "dataset_data": [ + "https://github.com/neheller/kits23#data-download", + ], + "license": ["CC BY-NC-SA 4.0"], + "paper": ["https://doi.org/10.48550/arXiv.2307.01984"], +} + +labels_map = { + "1": "kidney", + "2": "kidney tumor", + "3": "kidney cyst", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "corticomedullary-phase computed tomography (CT) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/KiTS23/preprocess_segmentation.py b/src/medvision_ds/datasets/KiTS23/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..2acfd6cc8ac8d172b4e7b66e647285367a740a75 --- /dev/null +++ b/src/medvision_ds/datasets/KiTS23/preprocess_segmentation.py @@ -0,0 +1,130 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "KiTS23", + "dataset_website": "https://kits-challenge.org/kits23/", + "dataset_data": [ + "https://github.com/neheller/kits23#data-download", + ], + "license": ["CC BY-NC-SA 4.0"], + "paper": ["https://doi.org/10.48550/arXiv.2307.01984"], +} + +labels_map = { + "1": "kidney", + "2": "kidney tumor", + "3": "kidney cyst", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "corticomedullary-phase computed tomography (CT) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/MSD/__init__.py b/src/medvision_ds/datasets/MSD/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/MSD/download.py b/src/medvision_ds/datasets/MSD/download.py new file mode 100644 index 0000000000000000000000000000000000000000..423dd2cf6dce1927c7ed8e90246075876f5baafe --- /dev/null +++ b/src/medvision_ds/datasets/MSD/download.py @@ -0,0 +1,195 @@ +import os +import shutil +import argparse +import glob +import urllib.request +import tarfile +from medvision_ds.utils.preprocess_utils import split_4d_nifti, move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: MSD +# Data: http://medicaldecathlon.com/dataaws/ +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # MSD task URLs and filenames + tasks = { + "BrainTumour": "Task01_BrainTumour.tar", + "Heart": "Task02_Heart.tar", + "Liver": "Task03_Liver.tar", + "Hippocampus": "Task04_Hippocampus.tar", + "Prostate": "Task05_Prostate.tar", + "Lung": "Task06_Lung.tar", + "Pancreas": "Task07_Pancreas.tar", + "HepaticVessel": "Task08_HepaticVessel.tar", + "Spleen": "Task09_Spleen.tar", + "Colon": "Task10_Colon.tar", + } + + base_url = "https://msd-for-monai.s3-us-west-2.amazonaws.com" + + # Download all tasks + print("Downloading MSD task datasets...") + for task_name, filename in tasks.items(): + url = f"{base_url}/{filename}" + print(f"Downloading {filename} from {url}...") + urllib.request.urlretrieve(url, filename) + print(f"Download complete: {filename}") + + # Extract tar files + print("Extracting downloaded files...") + for tar_file in glob.glob("*.tar"): + print(f"Extracting {tar_file}...") + with tarfile.open(tar_file, "r") as tar: + tar.extractall() + os.remove(tar_file) + print(f"{tar_file} extracted") + + # Rename task directories + for task_dir in glob.glob("Task*"): + new_name = f"MSD-{task_dir[7:]}" # Remove "Task??_" prefix + os.rename(task_dir, new_name) + + # Rename/remove subdirectories in MSD folders + for msd_dir in glob.glob("MSD-*"): + if not os.path.isdir(msd_dir): + continue + msd_dir = msd_dir.rstrip("/\\") + # Rename images and labels directories + os.rename(os.path.join(msd_dir, "imagesTr"), os.path.join(msd_dir, "Images")) + os.rename(os.path.join(msd_dir, "labelsTr"), os.path.join(msd_dir, "Masks")) + # Remove test images directory if it exists + test_dir = os.path.join(msd_dir, "imagesTs") + if os.path.exists(test_dir): + shutil.rmtree(test_dir) + + # Split 4D Nifti files in the MSD-BrainTumour dataset + split_4d_nifti(os.path.join("MSD-BrainTumour", "Images"), "MSD-BrainTumour") + shutil.rmtree(os.path.join("MSD-BrainTumour", "Images")) + # Rename numbered image folders to modality names in MSD-BrainTumour + modalities = ["FLAIR", "T1w", "T1gd", "T2w"] + for i, modality in enumerate(modalities, 1): + os.rename( + os.path.join("MSD-BrainTumour", f"Images-{i}"), + os.path.join("MSD-BrainTumour", f"Images-{modality}"), + ) + + # Split 4D Nifti files in the MSD-Prostate dataset + split_4d_nifti(os.path.join("MSD-Prostate", "Images"), "MSD-Prostate") + shutil.rmtree(os.path.join("MSD-Prostate", "Images")) + # Rename numbered image folders to modality names in MSD-Prostate + modalities = ["T2w", "ADC"] + for i, modality in enumerate(modalities, 1): + os.rename( + os.path.join("MSD-Prostate", f"Images-{i}"), + os.path.join("MSD-Prostate", f"Images-{modality}"), + ) + + # Clean up macOS and hidden files + for msd_dir in glob.glob("MSD-*"): + if not os.path.isdir(msd_dir): + continue + # Remove macOS metadata files + for pattern in ["._*", "._.DS_Store"]: + for file_path in glob.glob( + os.path.join(msd_dir, "**", pattern), recursive=True + ): + os.remove(file_path) + + # Remove specific hidden files + hidden_files = ["._dataset.json", "._imagesTr", "._imagesTs", "._labelsTr"] + for hf in hidden_files: + hf_path = os.path.join(msd_dir, hf) + if os.path.exists(hf_path): + os.remove(hf_path) + + # Clean up root hidden files + for msd_dir in glob.glob("MSD-*"): + json_file = os.path.join(msd_dir, "dataset.json") + if os.path.exists(json_file): + os.remove(json_file) + for f in glob.glob("._*"): + os.remove(f) + + # Move folder to dataset_dir + folders_to_move = [ + "MSD-BrainTumour", + "MSD-Colon", + "MSD-Heart", + "MSD-HepaticVessel", + "MSD-Hippocampus", + "MSD-Liver", + "MSD-Lung", + "MSD-Pancreas", + "MSD-Prostate", + "MSD-Spleen", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/MSD/preprocess_biometry.py b/src/medvision_ds/datasets/MSD/preprocess_biometry.py new file mode 100644 index 0000000000000000000000000000000000000000..c693bbaf296b4985d0c2b606b03f5685c896808f --- /dev/null +++ b/src/medvision_ds/datasets/MSD/preprocess_biometry.py @@ -0,0 +1,378 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerBiometry_fromSeg + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +CLUSTER_SIZE_THRESHOLD = 200 + +dataset_info = { + "dataset": "MSD", + "dataset_website": "http://medicaldecathlon.com/dataaws/", + "dataset_data": [ + "http://medicaldecathlon.com/dataaws/", + ], + "license": ["CC BY-SA 4.0"], + "paper": ["https://doi.org/10.1038/s41467-022-30695-9"], +} + +labels_map_BrainTumour = { + "1": "edema of brain", + "2": "non-enhancing brain tumor", + "3": "enhancing brain tumor", +} + +labels_map_Colon = {"1": "colon cancer primaries"} + +labels_map_Heart = {"1": "left atrium of heart"} + +labels_map_HepaticVessel = {"1": "liver vessel", "2": "liver tumor"} + +labels_map_Hippocampus = {"1": "anterior hippocampus", "2": "posterior hippocampus"} + +labels_map_Liver = {"1": "liver", "2": "liver cancer"} + +labels_map_Lung = { + "1": "lung cancer", +} + +labels_map_Pancreas = {"1": "pancreas", "2": "pancreas cancer"} + +labels_map_Prostate = { + "1": "peripheral zone of prostate", + "2": "transition zone of prostate", +} + +labels_map_Spleen = {"1": "spleen"} +# ==================================== + + +# =============== +# DO NOT CHANGE +# =============== +landmarks_map = { + "P1": "most right/anterior/superior endpoint of the major axis", + "P2": "most left/superior/inferior endpoint of the major axis", + "P3": "most right/anterior/superior endpoint of the minor axis", + "P4": "most left/superior/inferior endpoint of the minor axis", +} + +lines_map = { + "L-1-2": { + "name": "marjor axis of the fitted ellipse", + "element_keys": ["P1", "P2"], + "element_map_name": "landmarks_map", + }, + "L-3-4": { + "name": "minor axis of the fitted ellipse", + "element_keys": ["P3", "P4"], + "element_map_name": "landmarks_map", + }, +} + +angles_map = {} + +biometrics_map = [ + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-1-2", + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-3-4", + }, +] +# =============== + + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "gadolinium-enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-BrainTumour/Images-T1gd", + "mask_folder": "MSD-BrainTumour/Masks", + "landmark_folder": "MSD-BrainTumour/Landmarks-Label1", + "landmark_figure_folder": "MSD-BrainTumour/Landmarks-Label1-fig", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_BrainTumour, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 1, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "gadolinium-enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-BrainTumour/Images-T1gd", + "mask_folder": "MSD-BrainTumour/Masks", + "landmark_folder": "MSD-BrainTumour/Landmarks-Label2", + "landmark_figure_folder": "MSD-BrainTumour/Landmarks-Label2-fig", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_BrainTumour, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 2, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "MRI", + "image_description": "gadolinium-enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-BrainTumour/Images-T1gd", + "mask_folder": "MSD-BrainTumour/Masks", + "landmark_folder": "MSD-BrainTumour/Landmarks-Label3", + "landmark_figure_folder": "MSD-BrainTumour/Landmarks-Label3-fig", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_BrainTumour, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 3, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "MSD-Colon/Images", + "mask_folder": "MSD-Colon/Masks", + "landmark_folder": "MSD-Colon/Landmarks-Label1", + "landmark_figure_folder": "MSD-Colon/Landmarks-Label1-fig", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_Colon, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 1, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "MSD-HepaticVessel/Images", + "mask_folder": "MSD-HepaticVessel/Masks", + "landmark_folder": "MSD-HepaticVessel/Landmarks-Label2", + "landmark_figure_folder": "MSD-HepaticVessel/Landmarks-Label2-fig", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_HepaticVessel, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 2, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "MSD-Liver/Images", + "mask_folder": "MSD-Liver/Masks", + "landmark_folder": "MSD-Liver/Landmarks-Label2", + "landmark_figure_folder": "MSD-Liver/Landmarks-Label2-fig", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_Liver, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 2, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "CT", + "image_description": "chest computed tomography (CT) scan", + "image_folder": "MSD-Lung/Images", + "mask_folder": "MSD-Lung/Masks", + "landmark_folder": "MSD-Lung/Landmarks-Label1", + "landmark_figure_folder": "MSD-Lung/Landmarks-Label1-fig", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_Lung, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 1, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "MSD-Pancreas/Images", + "mask_folder": "MSD-Pancreas/Masks", + "landmark_folder": "MSD-Pancreas/Landmarks-Label2", + "landmark_figure_folder": "MSD-Pancreas/Landmarks-Label2-fig", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map_Pancreas, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 2, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + shrunken_bbox_scale=0.9, + enlarged_bbox_scale=1.1, + force_uint16_mask=False, + reorient2RAS=False, + visualization=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerBiometry_fromSeg( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + shrunk_bbox_scale=shrunken_bbox_scale, + enlarged_bbox_scale=enlarged_bbox_scale, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + visualization=visualization, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for biometric measurement task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--shrunken_bbox_scale", + type=float, + default=0.9, + help="Scale factor for shrunken bounding box", + ) + parser.add_argument( + "--enlarged_bbox_scale", + type=float, + default=1.1, + help="Scale factor for enlarged bounding box", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + parser.add_argument( + "--visualization", + action="store_true", + help="Enable visualization of the dataset processing", + ) + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + shrunken_bbox_scale=args.shrunken_bbox_scale, + enlarged_bbox_scale=args.enlarged_bbox_scale, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + visualization=args.visualization, + ) diff --git a/src/medvision_ds/datasets/MSD/preprocess_detection.py b/src/medvision_ds/datasets/MSD/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..bf52ca754530e0c865b324c9534acbaaba9dc1cc --- /dev/null +++ b/src/medvision_ds/datasets/MSD/preprocess_detection.py @@ -0,0 +1,296 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "MSD", + "dataset_website": "http://medicaldecathlon.com/dataaws/", + "dataset_data": [ + "http://medicaldecathlon.com/dataaws/", + ], + "license": ["CC BY-SA 4.0"], + "paper": ["https://doi.org/10.1038/s41467-022-30695-9"], +} + +labels_map_BrainTumour = { + "1": "edema of brain", + "2": "non-enhancing brain tumor", + "3": "enhancing brain tumor", +} + +labels_map_Colon = {"1": "colon cancer primaries"} + +labels_map_Heart = {"1": "left atrium of heart"} + +labels_map_HepaticVessel = {"1": "liver vessel", "2": "liver tumor"} + +labels_map_Hippocampus = {"1": "anterior hippocampus", "2": "posterior hippocampus"} + +labels_map_Liver = {"1": "liver", "2": "liver cancer"} + +labels_map_Lung = { + "1": "lung cancer", +} + +labels_map_Pancreas = {"1": "pancreas", "2": "pancreas cancer"} + +labels_map_Prostate = { + "1": "peripheral zone of prostate", + "2": "transition zone of prostate", +} + +labels_map_Spleen = {"1": "spleen"} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "fluid-attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-BrainTumour/Images-FLAIR", + "mask_folder": "MSD-BrainTumour/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_BrainTumour, + }, + { + "image_modality": "MRI", + "image_description": "T1 weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-BrainTumour/Images-T1w", + "mask_folder": "MSD-BrainTumour/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_BrainTumour, + }, + { + "image_modality": "MRI", + "image_description": "gadolinium-enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-BrainTumour/Images-T1gd", + "mask_folder": "MSD-BrainTumour/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_BrainTumour, + }, + { + "image_modality": "MRI", + "image_description": "T2 weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-BrainTumour/Images-T2w", + "mask_folder": "MSD-BrainTumour/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_BrainTumour, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "MSD-Colon/Images", + "mask_folder": "MSD-Colon/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Colon, + }, + { + "image_modality": "MRI", + "image_description": "cardiac magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-Heart/Images", + "mask_folder": "MSD-Heart/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Heart, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "MSD-HepaticVessel/Images", + "mask_folder": "MSD-HepaticVessel/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_HepaticVessel, + }, + { + "image_modality": "MRI", + "image_description": "hippocampus magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-Hippocampus/Images", + "mask_folder": "MSD-Hippocampus/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Hippocampus, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "MSD-Liver/Images", + "mask_folder": "MSD-Liver/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Liver, + }, + { + "image_modality": "CT", + "image_description": "chest computed tomography (CT) scan", + "image_folder": "MSD-Lung/Images", + "mask_folder": "MSD-Lung/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Lung, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "MSD-Pancreas/Images", + "mask_folder": "MSD-Pancreas/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Pancreas, + }, + { + "image_modality": "MRI", + "image_description": "T2-weighted prostate magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-Prostate/Images-T2w", + "mask_folder": "MSD-Prostate/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Prostate, + }, + { + "image_modality": "MRI", + "image_description": "appearance diffusion coefficient (ADC) map of prostate magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-Prostate/Images-ADC", + "mask_folder": "MSD-Prostate/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Prostate, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "MSD-Spleen/Images", + "mask_folder": "MSD-Spleen/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Spleen, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/MSD/preprocess_segmentation.py b/src/medvision_ds/datasets/MSD/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..eb21ddfafa4c9b284148bb33da417571839f78f5 --- /dev/null +++ b/src/medvision_ds/datasets/MSD/preprocess_segmentation.py @@ -0,0 +1,296 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "MSD", + "dataset_website": "http://medicaldecathlon.com/dataaws/", + "dataset_data": [ + "http://medicaldecathlon.com/dataaws/", + ], + "license": ["CC BY-SA 4.0"], + "paper": ["https://doi.org/10.1038/s41467-022-30695-9"], +} + +labels_map_BrainTumour = { + "1": "edema of brain", + "2": "non-enhancing brain tumor", + "3": "enhancing brain tumor", +} + +labels_map_Colon = {"1": "colon cancer primaries"} + +labels_map_Heart = {"1": "left atrium of heart"} + +labels_map_HepaticVessel = {"1": "liver vessel", "2": "liver tumor"} + +labels_map_Hippocampus = {"1": "anterior hippocampus", "2": "posterior hippocampus"} + +labels_map_Liver = {"1": "liver", "2": "liver cancer"} + +labels_map_Lung = { + "1": "lung cancer", +} + +labels_map_Pancreas = {"1": "pancreas", "2": "pancreas cancer"} + +labels_map_Prostate = { + "1": "peripheral zone of prostate", + "2": "transition zone of prostate", +} + +labels_map_Spleen = {"1": "spleen"} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "fluid-attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-BrainTumour/Images-FLAIR", + "mask_folder": "MSD-BrainTumour/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_BrainTumour, + }, + { + "image_modality": "MRI", + "image_description": "T1 weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-BrainTumour/Images-T1w", + "mask_folder": "MSD-BrainTumour/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_BrainTumour, + }, + { + "image_modality": "MRI", + "image_description": "gadolinium-enhanced T1-weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-BrainTumour/Images-T1gd", + "mask_folder": "MSD-BrainTumour/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_BrainTumour, + }, + { + "image_modality": "MRI", + "image_description": "T2 weighted brain magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-BrainTumour/Images-T2w", + "mask_folder": "MSD-BrainTumour/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_BrainTumour, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "MSD-Colon/Images", + "mask_folder": "MSD-Colon/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Colon, + }, + { + "image_modality": "MRI", + "image_description": "cardiac magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-Heart/Images", + "mask_folder": "MSD-Heart/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Heart, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "MSD-HepaticVessel/Images", + "mask_folder": "MSD-HepaticVessel/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_HepaticVessel, + }, + { + "image_modality": "MRI", + "image_description": "hippocampus magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-Hippocampus/Images", + "mask_folder": "MSD-Hippocampus/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Hippocampus, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "MSD-Liver/Images", + "mask_folder": "MSD-Liver/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Liver, + }, + { + "image_modality": "CT", + "image_description": "chest computed tomography (CT) scan", + "image_folder": "MSD-Lung/Images", + "mask_folder": "MSD-Lung/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Lung, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "MSD-Pancreas/Images", + "mask_folder": "MSD-Pancreas/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Pancreas, + }, + { + "image_modality": "MRI", + "image_description": "T2-weighted prostate magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-Prostate/Images-T2w", + "mask_folder": "MSD-Prostate/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Prostate, + }, + { + "image_modality": "MRI", + "image_description": "appearance diffusion coefficient (ADC) map of prostate magnetic resonance imaging (MRI) scan", + "image_folder": "MSD-Prostate/Images-ADC", + "mask_folder": "MSD-Prostate/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Prostate, + }, + { + "image_modality": "CT", + "image_description": "abdominal computed tomography (CT) scan", + "image_folder": "MSD-Spleen/Images", + "mask_folder": "MSD-Spleen/Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map_Spleen, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/OAIZIB_CM/__init__.py b/src/medvision_ds/datasets/OAIZIB_CM/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/OAIZIB_CM/download.py b/src/medvision_ds/datasets/OAIZIB_CM/download.py new file mode 100644 index 0000000000000000000000000000000000000000..c9522d4627d9542a75b556f6ad97b7bfdd80a4ee --- /dev/null +++ b/src/medvision_ds/datasets/OAIZIB_CM/download.py @@ -0,0 +1,129 @@ +import os +import shutil +import argparse +import glob +import zipfile +from huggingface_hub import snapshot_download +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: OAIZIB-CM +# Website: https://github.com/YongchengYAO/CartiMorph +# Data: https://huggingface.co/datasets/YongchengYAO/OAIZIB-CM +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download dataset + snapshot_download( + repo_id="YongchengYAO/OAIZIB-CM", + allow_patterns="*.zip", + repo_type="dataset", + revision="ba18c844309f6288b51772fd79a8f7cdb6aabc01", # commit hash on 2025-05-06 + local_dir=".", + max_workers=kwargs.get('max_workers', 1), + ) + + # Extract all zip files + for zip_file in glob.glob("*.zip"): + print(f"extracting {zip_file}") + with zipfile.ZipFile(zip_file, 'r') as zip_ref: + zip_ref.extractall('.') + os.remove(zip_file) + print(f"{zip_file} deleted") + + # Move test files to training directories + for f in glob.glob(os.path.join("imagesTs", "*.nii.gz")): + shutil.move(f, os.path.join("imagesTr", os.path.basename(f))) + for f in glob.glob(os.path.join("labelsTs", "*.nii.gz")): + shutil.move(f, os.path.join("labelsTr", os.path.basename(f))) + + # Rename directories to standard format + shutil.rmtree("Images") if os.path.exists("Images") else None + shutil.rmtree("Masks") if os.path.exists("Masks") else None + shutil.move("imagesTr", "Images") + shutil.move("labelsTr", "Masks") + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--max_workers", + type=int, + default=1, + help="Maximum number of workers for download", + ) + args = parser.parse_args() + + # Extract known arguments and pass the rest as kwargs + kwargs = {"max_workers": args.max_workers} + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + **kwargs + ) diff --git a/src/medvision_ds/datasets/OAIZIB_CM/preprocess_detection.py b/src/medvision_ds/datasets/OAIZIB_CM/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..005e6d7ec24240bb92d00166d0f5cd3f57208dc1 --- /dev/null +++ b/src/medvision_ds/datasets/OAIZIB_CM/preprocess_detection.py @@ -0,0 +1,135 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "OAIZIB-CM", + "dataset_website": "https://github.com/YongchengYAO/CartiMorph", + "dataset_data": [ + "https://huggingface.co/datasets/YongchengYAO/OAIZIB-CM", + ], + "license": ["CC BY-NC 4.0"], + "paper": [ + "https://doi.org/10.1016/j.media.2018.11.009", + "https://doi.org/10.1016/j.media.2023.103035", + "https://doi.org/10.1007/978-3-031-82007-6_16", + ], +} + +labels_map = { + "1": "femur", + "2": "femoral cartilage", + "3": "tibia", + "4": "medial tibial cartilage", + "5": "lateral tibial cartilage", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "knee magnetic resonance imaging (MRI) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/OAIZIB_CM/preprocess_segmentation.py b/src/medvision_ds/datasets/OAIZIB_CM/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..481b41cfb626f5a1e6532a688add1b261924a912 --- /dev/null +++ b/src/medvision_ds/datasets/OAIZIB_CM/preprocess_segmentation.py @@ -0,0 +1,135 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "OAIZIB-CM", + "dataset_website": "https://github.com/YongchengYAO/CartiMorph", + "dataset_data": [ + "https://huggingface.co/datasets/YongchengYAO/OAIZIB-CM", + ], + "license": ["CC BY-NC 4.0"], + "paper": [ + "https://doi.org/10.1016/j.media.2018.11.009", + "https://doi.org/10.1016/j.media.2023.103035", + "https://doi.org/10.1007/978-3-031-82007-6_16", + ], +} + +labels_map = { + "1": "femur", + "2": "femoral cartilage", + "3": "tibia", + "4": "medial tibial cartilage", + "5": "lateral tibial cartilage", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "knee magnetic resonance imaging (MRI) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/SKM_TEA/__init__.py b/src/medvision_ds/datasets/SKM_TEA/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/SKM_TEA/download.py b/src/medvision_ds/datasets/SKM_TEA/download.py new file mode 100644 index 0000000000000000000000000000000000000000..44eb4bcf07a45ce33b90e438db92d6d0f3b25c88 --- /dev/null +++ b/src/medvision_ds/datasets/SKM_TEA/download.py @@ -0,0 +1,123 @@ +import os +import shutil +import argparse +import zipfile +from huggingface_hub import hf_hub_download +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: SKM-TEA +# Data: https://aimi.stanford.edu/datasets/skm-tea-knee-mri +# Format: nii.gz (converted from raw data) +# ==================================== + + +# Define HuggingFace dataset ID +BiometricVQA_SKMTEA_HF_ID = os.environ.get( + "BiometricVQA_SKMTEA_HF_ID", "YongchengYAO/SKM-TEA-nii" +) + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download dataset from Hugging Face Hub + if BiometricVQA_SKMTEA_HF_ID == "YongchengYAO/SKM-TEA-nii": + hf_hub_download( + repo_id=BiometricVQA_SKMTEA_HF_ID, + filename="SKM-TEA-nii.zip", + repo_type="dataset", + revision="289ba731ea6b17e948210ce9cfbfaa95fa1ef236", # commit hash on 2025-02-01 + local_dir=".", + ) + else: + hf_hub_download( + repo_id=BiometricVQA_SKMTEA_HF_ID, + filename="SKM-TEA-nii.zip", + repo_type="dataset", + local_dir=".", + ) + + # Extract the downloaded zip file + with zipfile.ZipFile("SKM-TEA-nii.zip", "r") as zip_ref: + zip_ref.extractall() + + # Rename directories to match standard format + shutil.rmtree("Images-E1") if os.path.exists("Images-E1") else None + shutil.rmtree("Images-E2") if os.path.exists("Images-E2") else None + shutil.rmtree("Masks") if os.path.exists("Masks") else None + shutil.move(os.path.join("SKM-TEA-nii", "img_nii_E1"), "Images-E1") + shutil.move(os.path.join("SKM-TEA-nii", "img_nii_E2"), "Images-E2") + shutil.move(os.path.join("SKM-TEA-nii", "seg-nii"), "Masks") + + # Move folder to dataset_dir + folders_to_move = [ + "Images-E1", + "Images-E2", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/SKM_TEA/preprocess_detection.py b/src/medvision_ds/datasets/SKM_TEA/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..f215458308ec55077471595e85deb6fe0625b240 --- /dev/null +++ b/src/medvision_ds/datasets/SKM_TEA/preprocess_detection.py @@ -0,0 +1,144 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "SKM-TEA", + "dataset_website": "https://aimi.stanford.edu/datasets/skm-tea-knee-mri", + "dataset_data": [ + "https://aimi.stanford.edu/datasets/skm-tea-knee-mri", + ], + "license": [""], + "paper": ["https://openreview.net/pdf?id=YDMFgD_qJuA"], +} + +labels_map = { + "1": "patellar cartilage", + "2": "femoral cartilage", + "3": "medial tibial cartilage", + "4": "lateral tibial cartilage", + "5": "medial meniscus", + "6": "lateral meniscus", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "knee magnetic resonance imaging (MRI) scan", + "image_folder": "Images-E1", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + { + "image_modality": "MRI", + "image_description": "knee magnetic resonance imaging (MRI) scan", + "image_folder": "Images-E2", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/SKM_TEA/preprocess_segmentation.py b/src/medvision_ds/datasets/SKM_TEA/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..69aa31fb3f8bbb2f7fba5565857ed2914615f246 --- /dev/null +++ b/src/medvision_ds/datasets/SKM_TEA/preprocess_segmentation.py @@ -0,0 +1,144 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "SKM-TEA", + "dataset_website": "https://aimi.stanford.edu/datasets/skm-tea-knee-mri", + "dataset_data": [ + "https://aimi.stanford.edu/datasets/skm-tea-knee-mri", + ], + "license": [""], + "paper": ["https://openreview.net/pdf?id=YDMFgD_qJuA"], +} + +labels_map = { + "1": "patellar cartilage", + "2": "femoral cartilage", + "3": "medial tibial cartilage", + "4": "lateral tibial cartilage", + "5": "medial meniscus", + "6": "lateral meniscus", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "knee magnetic resonance imaging (MRI) scan", + "image_folder": "Images-E1", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + { + "image_modality": "MRI", + "image_description": "knee magnetic resonance imaging (MRI) scan", + "image_folder": "Images-E2", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": ".nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/ToothFairy2/__init__.py b/src/medvision_ds/datasets/ToothFairy2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/ToothFairy2/download.py b/src/medvision_ds/datasets/ToothFairy2/download.py new file mode 100644 index 0000000000000000000000000000000000000000..4fbb2e78ce8e14a8bfa8658531b9e978abee137e --- /dev/null +++ b/src/medvision_ds/datasets/ToothFairy2/download.py @@ -0,0 +1,119 @@ +import os +import shutil +import argparse +import zipfile +from huggingface_hub import hf_hub_download +from medvision_ds.utils.data_conversion import convert_mha_to_nifti +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: ToothFairy2 +# Data: https://ditto.ing.unimore.it/toothfairy2/ +# Format: mha +# ==================================== + + +# Define HuggingFace dataset ID +BiometricVQA_ToothFairy2_HF_ID = os.environ.get( + "BiometricVQA_ToothFairy2_HF_ID", "YongchengYAO/ToothFairy2" +) + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download dataset from Hugging Face Hub + if BiometricVQA_ToothFairy2_HF_ID == "YongchengYAO/ToothFairy2": + hf_hub_download( + repo_id=BiometricVQA_ToothFairy2_HF_ID, + filename="ToothFairy2.zip", + repo_type="dataset", + revision="24c4066d9868d468c5bedf3d7045ed78f993c20c", # commit hash on 2025-02-01 + local_dir=".", + ) + else: + hf_hub_download( + repo_id=BiometricVQA_ToothFairy2_HF_ID, + filename="ToothFairy2.zip", + repo_type="dataset", + local_dir=".", + ) + + # Extract the downloaded zip file + with zipfile.ZipFile("ToothFairy2.zip", "r") as zip_ref: + zip_ref.extractall() + + # Rename directories to match standard format using Windows path conventions + convert_mha_to_nifti(os.path.join("ToothFairy2", "imagesTr"), "Images") + convert_mha_to_nifti(os.path.join("ToothFairy2", "labelsTr"), "Masks") + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/ToothFairy2/preprocess_detection.py b/src/medvision_ds/datasets/ToothFairy2/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..986e3722b35e57db99797ff8a219debd94a2bc25 --- /dev/null +++ b/src/medvision_ds/datasets/ToothFairy2/preprocess_detection.py @@ -0,0 +1,173 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "ToothFairy2", + "dataset_website": "https://toothfairy2.grand-challenge.org", + "dataset_data": [ + "https://ditto.ing.unimore.it/toothfairy2/", + ], + "license": ["CC BY-SA"], + "paper": [ + "https://doi.org/10.1109/TMI.2024.3523096", + "https://doi.org/10.1109/ACCESS.2024.3408629", + "https://doi.org/10.1109/CVPR52688.2022.02046", + ], +} + +labels_map = { + "1": "lower jawbone", + "2": "upper jawbone", + "3": "left inferior alveolar canal", + "4": "right inferior alveolar canal", + "5": "left maxillary sinus", + "6": "right maxillary sinus", + "7": "pharynx", + "8": "bridge", + "9": "crown", + "10": "implant", + "11": "upper right central incisor", + "12": "upper right lateral incisor", + "13": "upper right canine", + "14": "upper right first premolar", + "15": "upper right second premolar", + "16": "upper right first molar", + "17": "upper right second molar", + "18": "upper right third molar (wisdom tooth)", + "21": "upper left central incisor", + "22": "upper left lateral incisor", + "23": "upper left canine", + "24": "upper left first premolar", + "25": "upper left second premolar", + "26": "upper left first molar", + "27": "upper left second molar", + "28": "upper left third molar (wisdom tooth)", + "31": "lower left central incisor", + "32": "lower left lateral incisor", + "33": "lower left canine", + "34": "lower left first premolar", + "35": "lower left second premolar", + "36": "lower left first molar", + "37": "lower left second molar", + "38": "lower left third molar (wisdom tooth)", + "40": "na", + "41": "lower right central incisor", + "42": "lower right lateral incisor", + "43": "lower right canine", + "44": "lower right first premolar", + "45": "lower right second premolar", + "46": "lower right first molar", + "47": "lower right second molar", + "48": "lower right third molar (wisdom tooth)", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "cone beam computed tomography (CT) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/ToothFairy2/preprocess_segmentation.py b/src/medvision_ds/datasets/ToothFairy2/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..7b8f27b7bd63f5a4d18d4cdc9998c0b388b873a6 --- /dev/null +++ b/src/medvision_ds/datasets/ToothFairy2/preprocess_segmentation.py @@ -0,0 +1,174 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "ToothFairy2", + "dataset_website": "https://toothfairy2.grand-challenge.org", + "dataset_data": [ + "https://ditto.ing.unimore.it/toothfairy2/", + ], + "license": ["CC BY-SA"], + "paper": [ + "https://doi.org/10.1109/TMI.2024.3523096", + "https://doi.org/10.1109/ACCESS.2024.3408629", + "https://doi.org/10.1109/CVPR52688.2022.02046", + ], +} + +labels_map = { + "1": "lower jawbone", + "2": "upper jawbone", + "3": "left inferior alveolar canal", + "4": "right inferior alveolar canal", + "5": "left maxillary sinus", + "6": "right maxillary sinus", + "7": "pharynx", + "8": "bridge", + "9": "crown", + "10": "implant", + "11": "upper right central incisor", + "12": "upper right lateral incisor", + "13": "upper right canine", + "14": "upper right first premolar", + "15": "upper right second premolar", + "16": "upper right first molar", + "17": "upper right second molar", + "18": "upper right third molar (wisdom tooth)", + "21": "upper left central incisor", + "22": "upper left lateral incisor", + "23": "upper left canine", + "24": "upper left first premolar", + "25": "upper left second premolar", + "26": "upper left first molar", + "27": "upper left second molar", + "28": "upper left third molar (wisdom tooth)", + "31": "lower left central incisor", + "32": "lower left lateral incisor", + "33": "lower left canine", + "34": "lower left first premolar", + "35": "lower left second premolar", + "36": "lower left first molar", + "37": "lower left second molar", + "38": "lower left third molar (wisdom tooth)", + "40": "na", + "41": "lower right central incisor", + "42": "lower right lateral incisor", + "43": "lower right canine", + "44": "lower right first premolar", + "45": "lower right second premolar", + "46": "lower right first molar", + "47": "lower right second molar", + "48": "lower right third molar (wisdom tooth)", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "cone beam computed tomography (CT) scan", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/TopCoW24/__init__.py b/src/medvision_ds/datasets/TopCoW24/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/TopCoW24/download.py b/src/medvision_ds/datasets/TopCoW24/download.py new file mode 100644 index 0000000000000000000000000000000000000000..6e61d3a745f4202756452f873205ba15d023f5e0 --- /dev/null +++ b/src/medvision_ds/datasets/TopCoW24/download.py @@ -0,0 +1,122 @@ +import os +import shutil +import argparse +import glob +import zipfile +from huggingface_hub import hf_hub_download +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: TopCoW24 +# Challenge: https://topcow24.grand-challenge.org +# Data (official): https://drive.switch.ch/index.php/s/rkqOO3adjmJVlMz +# HF Data: https://huggingface.co/datasets/YongchengYAO/TopCoW24-Seg +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download dataset from Hugging Face Hub + hf_hub_download( + repo_id="YongchengYAO/TopCoW24-Seg", + filename="TopCoW24.zip", + repo_type="dataset", + revision="53469cf5998bd051d29803c6660d4cd2210214bc", # commit hash on 2025-02-20 + local_dir=".", + ) + + # Extract the downloaded zip file + with zipfile.ZipFile("TopCoW24.zip", 'r') as zip_ref: + zip_ref.extractall() + + # Rename directories to standard format + shutil.rmtree("Images") if os.path.exists("Images") else None + shutil.rmtree("Masks") if os.path.exists("Masks") else None + shutil.move(os.path.join("TopCoW24", "cow_seg_labelsTr"), "Masks") + shutil.move(os.path.join("TopCoW24", "imagesTr"), "Images") + # Create directories for CT and MR images and masks + for modality in ["CT", "MR"]: + for folder in ["Images", "Masks"]: + os.makedirs(os.path.join(f"TopCoW24-{modality}", folder), exist_ok=True) + + # Move files based on modality (CT/MR) and type (Images/Masks) + for folder in ["Images", "Masks"]: + ct_files = glob.glob(os.path.join(folder, "topcow_ct*.nii.gz")) + mr_files = glob.glob(os.path.join(folder, "topcow_mr*.nii.gz")) + for file in ct_files: + shutil.move(file, os.path.join("TopCoW24-CT", folder)) + for file in mr_files: + shutil.move(file, os.path.join("TopCoW24-MR", folder)) + + # Move folder to dataset_dir + folders_to_move = [ + "TopCoW24-CT", + "TopCoW24-MR", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/TopCoW24/preprocess_detection.py b/src/medvision_ds/datasets/TopCoW24/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..438e1816954b10b22c77abb1042d03418bf81020 --- /dev/null +++ b/src/medvision_ds/datasets/TopCoW24/preprocess_detection.py @@ -0,0 +1,151 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "TopCoW24", + "dataset_website": "https://topcow24.grand-challenge.org", + "dataset_data": [ + "https://drive.switch.ch/index.php/s/rkqOO3adjmJVlMz", + ], + "license": [""], + "paper": ["https://doi.org/10.48550/arXiv.2312.17670"], +} + +labels_map = { + "1": "basilar artery", + "2": "right posterior cerebral artery", + "3": "left posterior cerebral artery", + "4": "right internal carotid arterr", + "5": "right middle cerebral artery", + "6": "left internal carotid artery", + "7": "left middle cerebral artery", + "8": "right posterior communicating artery", + "9": "left posterior communicating artery", + "10": "anterior communicating artery", + "11": "right anterior cerebral artery", + "12": "left anterior cerebral artery", + "15": "third a2 segment", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "Time of Flight Magnetic Resonance Angiography (TOF-MRA) scan", + "image_folder": "TopCoW24-MR/Images", + "mask_folder": "TopCoW24-MR/Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + { + "image_modality": "CT", + "image_description": "Computed Tomography Angiography (CTA) scan", + "image_folder": "TopCoW24-CT/Images", + "mask_folder": "TopCoW24-CT/Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/TopCoW24/preprocess_segmentation.py b/src/medvision_ds/datasets/TopCoW24/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..20fb7a72997ececc459d3070a125f0a188dc5ae4 --- /dev/null +++ b/src/medvision_ds/datasets/TopCoW24/preprocess_segmentation.py @@ -0,0 +1,151 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "TopCoW24", + "dataset_website": "https://topcow24.grand-challenge.org", + "dataset_data": [ + "https://drive.switch.ch/index.php/s/rkqOO3adjmJVlMz", + ], + "license": [""], + "paper": ["https://doi.org/10.48550/arXiv.2312.17670"], +} + +labels_map = { + "1": "basilar artery", + "2": "right posterior cerebral artery", + "3": "left posterior cerebral artery", + "4": "right internal carotid artery", + "5": "right middle cerebral artery", + "6": "left internal carotid artery", + "7": "left middle cerebral artery", + "8": "right posterior communicating artery", + "9": "left posterior communicating artery", + "10": "anterior communicating artery", + "11": "right anterior cerebral artery", + "12": "left anterior cerebral artery", + "15": "third a2 segment", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "Time of Flight Magnetic Resonance Angiography (TOF-MRA) scan", + "image_folder": "TopCoW24-MR/Images", + "mask_folder": "TopCoW24-MR/Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + { + "image_modality": "CT", + "image_description": "Computed Tomography Angiography (CTA) scan", + "image_folder": "TopCoW24-CT/Images", + "mask_folder": "TopCoW24-CT/Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/TotalSegmentator/__init__.py b/src/medvision_ds/datasets/TotalSegmentator/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/TotalSegmentator/download_fast.py b/src/medvision_ds/datasets/TotalSegmentator/download_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..883d94d36284d32adf22a4e47d83bfa5fc40c48e --- /dev/null +++ b/src/medvision_ds/datasets/TotalSegmentator/download_fast.py @@ -0,0 +1,136 @@ +import os +import argparse +import shutil +import zipfile +from huggingface_hub import hf_hub_download +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: TotalSegmentator +# GitHub: https://github.com/wasserth/TotalSegmentator +# Data: CT: https://zenodo.org/records/10047292 +# MR: https://zenodo.org/records/14710732 +# Preprocessed Data: +# CT: https://huggingface.co/datasets/YongchengYAO/TotalSegmentator-CT-Lite +# MR: https://huggingface.co/datasets/YongchengYAO/TotalSegmentator-MR-Lite +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download CT dataset from Hugging Face Hub + ct_dir = os.path.join(tmp_dir, "TotalSegmentator-CT") + os.makedirs(ct_dir, exist_ok=True) + hf_hub_download( + repo_id="YongchengYAO/TotalSegmentator-CT-Lite", + filename="Images.zip", + repo_type="dataset", + revision="cbbab480af869bf7abc3f4c86da56ef5f6436232", # commit hash on 2025-04-11 + local_dir=ct_dir, + ) + hf_hub_download( + repo_id="YongchengYAO/TotalSegmentator-CT-Lite", + filename="Masks.zip", + repo_type="dataset", + revision="cbbab480af869bf7abc3f4c86da56ef5f6436232", # commit hash on 2025-04-11 + local_dir=ct_dir, + ) + + # Download MR dataset from Hugging Face Hub + mr_dir = os.path.join(tmp_dir, "TotalSegmentator-MR") + os.makedirs(mr_dir, exist_ok=True) + hf_hub_download( + repo_id="YongchengYAO/TotalSegmentator-MR-Lite", + filename="Images.zip", + repo_type="dataset", + revision="6d6135d58049c7f5dc694be43b7f0870b55f2392", # commit hash on 2025-04-11 + local_dir=mr_dir, + ) + hf_hub_download( + repo_id="YongchengYAO/TotalSegmentator-MR-Lite", + filename="Masks.zip", + repo_type="dataset", + revision="6d6135d58049c7f5dc694be43b7f0870b55f2392", # commit hash on 2025-04-11 + local_dir=mr_dir, + ) + + # Extract CT datasets + print("Extracting files... This may take some time.") + with zipfile.ZipFile(os.path.join(ct_dir, "Images.zip"), 'r') as zip_ref: + zip_ref.extractall(ct_dir) + with zipfile.ZipFile(os.path.join(ct_dir, "Masks.zip"), 'r') as zip_ref: + zip_ref.extractall(ct_dir) + + # Extract MR datasets + print("Extracting files... This may take some time.") + with zipfile.ZipFile(os.path.join(mr_dir, "Images.zip"), 'r') as zip_ref: + zip_ref.extractall(mr_dir) + with zipfile.ZipFile(os.path.join(mr_dir, "Masks.zip"), 'r') as zip_ref: + zip_ref.extractall(mr_dir) + + # Move folder to dataset_dir + folders_to_move = [ + "TotalSegmentator-CT", + "TotalSegmentator-MR", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/TotalSegmentator/download_raw.py b/src/medvision_ds/datasets/TotalSegmentator/download_raw.py new file mode 100644 index 0000000000000000000000000000000000000000..e030de2b93299d71f13942f213b505c513454216 --- /dev/null +++ b/src/medvision_ds/datasets/TotalSegmentator/download_raw.py @@ -0,0 +1,356 @@ +import os +import shutil +import argparse +import zipfile +import urllib.request +import nibabel as nib +import numpy as np +from pathlib import Path + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: TotalSegmentator +# GitHub: https://github.com/wasserth/TotalSegmentator +# Data: CT: https://zenodo.org/records/10047292 +# MR: https://zenodo.org/records/14710732 +# Preprocessed Data: +# CT: https://huggingface.co/datasets/YongchengYAO/TotalSegmentator-CT-Lite +# MR: https://huggingface.co/datasets/YongchengYAO/TotalSegmentator-MR-Lite +# Format: nii.gz +# ==================================== + +# -------------------------- +# DO NOT CHANGE THE LABELS +# -------------------------- +# Define the dictionary mapping index to TotalSegmentator name +labels_CT = { + 1: "spleen", + 2: "kidney_right", + 3: "kidney_left", + 4: "gallbladder", + 5: "liver", + 6: "stomach", + 7: "pancreas", + 8: "adrenal_gland_right", + 9: "adrenal_gland_left", + 10: "lung_upper_lobe_left", + 11: "lung_lower_lobe_left", + 12: "lung_upper_lobe_right", + 13: "lung_middle_lobe_right", + 14: "lung_lower_lobe_right", + 15: "esophagus", + 16: "trachea", + 17: "thyroid_gland", + 18: "small_bowel", + 19: "duodenum", + 20: "colon", + 21: "urinary_bladder", + 22: "prostate", + 23: "kidney_cyst_left", + 24: "kidney_cyst_right", + 25: "sacrum", + 26: "vertebrae_S1", + 27: "vertebrae_L5", + 28: "vertebrae_L4", + 29: "vertebrae_L3", + 30: "vertebrae_L2", + 31: "vertebrae_L1", + 32: "vertebrae_T12", + 33: "vertebrae_T11", + 34: "vertebrae_T10", + 35: "vertebrae_T9", + 36: "vertebrae_T8", + 37: "vertebrae_T7", + 38: "vertebrae_T6", + 39: "vertebrae_T5", + 40: "vertebrae_T4", + 41: "vertebrae_T3", + 42: "vertebrae_T2", + 43: "vertebrae_T1", + 44: "vertebrae_C7", + 45: "vertebrae_C6", + 46: "vertebrae_C5", + 47: "vertebrae_C4", + 48: "vertebrae_C3", + 49: "vertebrae_C2", + 50: "vertebrae_C1", + 51: "heart", + 52: "aorta", + 53: "pulmonary_vein", + 54: "brachiocephalic_trunk", + 55: "subclavian_artery_right", + 56: "subclavian_artery_left", + 57: "common_carotid_artery_right", + 58: "common_carotid_artery_left", + 59: "brachiocephalic_vein_left", + 60: "brachiocephalic_vein_right", + 61: "atrial_appendage_left", + 62: "superior_vena_cava", + 63: "inferior_vena_cava", + 64: "portal_vein_and_splenic_vein", + 65: "iliac_artery_left", + 66: "iliac_artery_right", + 67: "iliac_vena_left", + 68: "iliac_vena_right", + 69: "humerus_left", + 70: "humerus_right", + 71: "scapula_left", + 72: "scapula_right", + 73: "clavicula_left", + 74: "clavicula_right", + 75: "femur_left", + 76: "femur_right", + 77: "hip_left", + 78: "hip_right", + 79: "spinal_cord", + 80: "gluteus_maximus_left", + 81: "gluteus_maximus_right", + 82: "gluteus_medius_left", + 83: "gluteus_medius_right", + 84: "gluteus_minimus_left", + 85: "gluteus_minimus_right", + 86: "autochthon_left", + 87: "autochthon_right", + 88: "iliopsoas_left", + 89: "iliopsoas_right", + 90: "brain", + 91: "skull", + 92: "rib_left_1", + 93: "rib_left_2", + 94: "rib_left_3", + 95: "rib_left_4", + 96: "rib_left_5", + 97: "rib_left_6", + 98: "rib_left_7", + 99: "rib_left_8", + 100: "rib_left_9", + 101: "rib_left_10", + 102: "rib_left_11", + 103: "rib_left_12", + 104: "rib_right_1", + 105: "rib_right_2", + 106: "rib_right_3", + 107: "rib_right_4", + 108: "rib_right_5", + 109: "rib_right_6", + 110: "rib_right_7", + 111: "rib_right_8", + 112: "rib_right_9", + 113: "rib_right_10", + 114: "rib_right_11", + 115: "rib_right_12", + 116: "sternum", + 117: "costal_cartilages", +} + + +labels_MR = { + 1: "spleen", + 2: "kidney_right", + 3: "kidney_left", + 4: "gallbladder", + 5: "liver", + 6: "stomach", + 7: "pancreas", + 8: "adrenal_gland_right", + 9: "adrenal_gland_left", + 10: "lung_left", + 11: "lung_right", + 12: "esophagus", + 13: "small_bowel", + 14: "duodenum", + 15: "colon", + 16: "urinary_bladder", + 17: "prostate", + 18: "sacrum", + 19: "vertebrae", + 20: "intervertebral_discs", + 21: "spinal_cord", + 22: "heart", + 23: "aorta", + 24: "inferior_vena_cava", + 25: "portal_vein_and_splenic_vein", + 26: "iliac_artery_left", + 27: "iliac_artery_right", + 28: "iliac_vena_left", + 29: "iliac_vena_right", + 30: "humerus_left", + 31: "humerus_right", + 32: "scapula_left", + 33: "scapula_right", + 34: "clavicula_left", + 35: "clavicula_right", + 36: "femur_left", + 37: "femur_right", + 38: "hip_left", + 39: "hip_right", + 40: "gluteus_maximus_left", + 41: "gluteus_maximus_right", + 42: "gluteus_medius_left", + 43: "gluteus_medius_right", + 44: "gluteus_minimus_left", + 45: "gluteus_minimus_right", + 46: "autochthon_left", + 47: "autochthon_right", + 48: "iliopsoas_left", + 49: "iliopsoas_right", + 50: "brain", +} +# -------------------------- + + +def process_data_totalsegmentator(subject_path: Path, output_dir: Path, modality: str): + """Process a single subject folder.""" + print(f"Processing subject: {subject_path.name}") + + # Make dirs + masks_dir = output_dir / "Masks" + images_dir = output_dir / "Images" + os.makedirs(masks_dir, exist_ok=True) + os.makedirs(images_dir, exist_ok=True) + + # Initialize output image with zeros + seg_dir = subject_path / "segmentations" + if not seg_dir.exists(): + print(f"No segmentations directory found in {subject_path}") + return + + # Get image file + if modality == "CT": + img_file = subject_path / "ct.nii.gz" + elif modality == "MR": + img_file = subject_path / "mri.nii.gz" + else: + print(f"Invalid modality: {modality}") + return + + # Get reference image to copy metadata + ref_img = nib.load(str(img_file)) + combined_array = np.zeros(ref_img.shape, dtype=np.int16) + + # Get segmentation labels + if modality == "CT": + seg_labels = labels_CT + elif modality == "MR": + seg_labels = labels_MR + else: + print(f"Invalid modality: {modality}") + return + + # Process each segmentation + for index, organ in seg_labels.items(): + seg_file = seg_dir / f"{organ}.nii.gz" + if seg_file.exists(): + print(f"Processing {organ}") + # Read binary mask + mask = nib.load(str(seg_file)) + mask_array = mask.get_fdata().astype(np.int16) + # Find overlapping regions and set them to 0 in the new mask + overlap = (mask_array == 1) & (combined_array > 0) + mask_array[overlap] = 0 + # Replace remaining 1s with index + mask_array[mask_array == 1] = index + # Add to combined mask + combined_array += mask_array + + # Save combined mask + output_file = masks_dir / f"{subject_path.name}.nii.gz" + combined_nifti = nib.Nifti1Image(combined_array, ref_img.affine, ref_img.header) + nib.save(combined_nifti, str(output_file)) + print(f"Saved combined mask to {output_file}") + + if img_file.exists(): + shutil.copy2(img_file, images_dir / f"{subject_path.name}.nii.gz") + print(f"Copied {modality} image for {subject_path.name}") + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + # Download files + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download dataset from Zenodo + # Download CT dataset + print("Downloading TotalSegmentator CT dataset...") + urllib.request.urlretrieve( + "https://zenodo.org/records/10047292/files/Totalsegmentator_dataset_v201.zip", + "TotalSegmentator-CT.zip" + ) + # Download MR dataset + print("Downloading TotalSegmentator MR dataset...") + urllib.request.urlretrieve( + "https://zenodo.org/records/14710732/files/TotalsegmentatorMRI_dataset_v200.zip", + "TotalSegmentator-MR.zip" + ) + + # Extract CT dataset + print("Extracting TotalSegmentator CT dataset...") + with zipfile.ZipFile("TotalSegmentator-CT.zip", 'r') as zip_ref: + zip_ref.extractall("TotalSegmentator-CT-raw") + + # Extract MR dataset + print("Extracting TotalSegmentator MR dataset...") + with zipfile.ZipFile("TotalSegmentator-MR.zip", 'r') as zip_ref: + zip_ref.extractall("TotalSegmentator-MR-raw") + + data_dir = Path.cwd() + + # Process CT data + ct_out_dir = data_dir / "TotalSegmentator-CT" + ct_working_dir = data_dir / "TotalSegmentator-CT-raw" + for item in ct_working_dir.iterdir(): + process_data_totalsegmentator(item, ct_out_dir, "CT") + + # Process MR data + mr_out_dir = data_dir / "TotalSegmentator-MR" + mr_working_dir = data_dir / "TotalSegmentator-MR-raw" + for item in mr_working_dir.iterdir(): + process_data_totalsegmentator(item, mr_out_dir, "MR") + + # Clean up + shutil.rmtree("TotalSegmentator-CT-raw") + shutil.rmtree("TotalSegmentator-MR-raw") + os.remove("TotalSegmentator-CT.zip") + os.remove("TotalSegmentator-MR.zip") + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/TotalSegmentator/preprocess_detection.py b/src/medvision_ds/datasets/TotalSegmentator/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..83162f2b13fd001dd886ed049e2152ad85061ffe --- /dev/null +++ b/src/medvision_ds/datasets/TotalSegmentator/preprocess_detection.py @@ -0,0 +1,308 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "TotalSegmentator", + "dataset_website": "https://github.com/wasserth/TotalSegmentator", + "dataset_data": [ + "https://zenodo.org/records/10047292", # CT + "https://zenodo.org/records/14710732", # MR + ], + "license": ["CC BY 4.0", "CC BY-NC-SA 2.0"], + "paper": ["https://doi.org/10.1148/ryai.230024"], +} + +labels_map_CT = { + "1": "spleen", + "2": "right kidney", + "3": "left kidney", + "4": "gallbladder", + "5": "liver", + "6": "stomach", + "7": "pancreas", + "8": "right adrenal gland", + "9": "left adrenal gland", + "10": "left lung upper lobe", + "11": "left lung lower lobe", + "12": "right lung upper lobe", + "13": "right lung middle lobe", + "14": "right lung lower lobe", + "15": "esophagus", + "16": "trachea", + "17": "thyroid gland", + "18": "small bowel", + "19": "duodenum", + "20": "colon", + "21": "urinary bladder", + "22": "prostate", + "23": "left kidney cyst", + "24": "right kidney cyst", + "25": "sacrum", + "26": "vertebra S1", + "27": "vertebra L5", + "28": "vertebra L4", + "29": "vertebra L3", + "30": "vertebra L2", + "31": "vertebra L1", + "32": "vertebra T12", + "33": "vertebra T11", + "34": "vertebra T10", + "35": "vertebra T9", + "36": "vertebra T8", + "37": "vertebra T7", + "38": "vertebra T6", + "39": "vertebra T5", + "40": "vertebra T4", + "41": "vertebra T3", + "42": "vertebra T2", + "43": "vertebra T1", + "44": "vertebra C7", + "45": "vertebra C6", + "46": "vertebra C5", + "47": "vertebra C4", + "48": "vertebra C3", + "49": "vertebra C2", + "50": "vertebra C1", + "51": "heart", + "52": "aorta", + "53": "pulmonary vein", + "54": "brachiocephalic trunk", + "55": "right subclavian artery", + "56": "left subclavian artery", + "57": "right common carotid artery", + "58": "left common carotid artery", + "59": "left brachiocephalic vein", + "60": "right brachiocephalic vein", + "61": "left atrial appendage", + "62": "superior vena cava", + "63": "inferior vena cava", + "64": "portal vein and splenic vein", + "65": "left iliac artery", + "66": "right iliac artery", + "67": "left iliac vein", + "68": "right iliac vein", + "69": "left humerus", + "70": "right humerus", + "71": "left scapula", + "72": "right scapula", + "73": "left clavicle", + "74": "right clavicle", + "75": "left femur", + "76": "right femur", + "77": "left hip", + "78": "right hip", + "79": "spinal cord", + "80": "left gluteus maximus", + "81": "right gluteus maximus", + "82": "left gluteus medius", + "83": "right gluteus medius", + "84": "left gluteus minimus", + "85": "right gluteus minimus", + "86": "left autochthon", + "87": "right autochthon", + "88": "left iliopsoas", + "89": "right iliopsoas", + "90": "brain", + "91": "skull", + "92": "left 1st rib", + "93": "left 2nd rib", + "94": "left 3rd rib", + "95": "left 4th rib", + "96": "left 5th rib", + "97": "left 6th rib", + "98": "left 7th rib", + "99": "left 8th rib", + "100": "left 9th rib", + "101": "left 10th rib", + "102": "left 11th rib", + "103": "left 12th rib", + "104": "right 1st rib", + "105": "right 2nd rib", + "106": "right 3rd rib", + "107": "right 4th rib", + "108": "right 5th rib", + "109": "right 6th rib", + "110": "right 7th rib", + "111": "right 8th rib", + "112": "right 9th rib", + "113": "right 10th rib", + "114": "right 11th rib", + "115": "right 12th rib", + "116": "sternum", + "117": "costal cartilages", +} + +labels_map_MR = { + "1": "spleen", + "2": "right kidney", + "3": "left kidney", + "4": "gallbladder", + "5": "liver", + "6": "stomach", + "7": "pancreas", + "8": "right adrenal gland", + "9": "left adrenal gland", + "10": "left lung", + "11": "right lung", + "12": "esophagus", + "13": "small bowel", + "14": "duodenum", + "15": "colon", + "16": "urinary bladder", + "17": "prostate", + "18": "sacrum", + "19": "vertebrae", + "20": "intervertebral discs", + "21": "spinal cord", + "22": "heart", + "23": "aorta", + "24": "inferior vena cava", + "25": "portal and splenic veins", + "26": "left iliac artery", + "27": "right iliac artery", + "28": "left iliac vein", + "29": "right iliac vein", + "30": "left humerus", + "31": "right humerus", + "32": "left scapula", + "33": "right scapula", + "34": "left clavicle", + "35": "right clavicle", + "36": "left femur", + "37": "right femur", + "38": "left hip", + "39": "right hip", + "40": "left gluteus maximus", + "41": "right gluteus maximus", + "42": "left gluteus medius", + "43": "right gluteus medius", + "44": "left gluteus minimus", + "45": "right gluteus minimus", + "46": "left autochthon", + "47": "right autochthon", + "48": "left iliopsoas", + "49": "right iliopsoas", + "50": "brain", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "computed tomography (CT) scan", + "image_folder": "TotalSegmentator-CT/Images", # Directory containing the .nii.gz image files + "mask_folder": "TotalSegmentator-CT/Masks", # Directory containing the mask files + "image_prefix": "", # String before case ID in image filename (e.g., "" for "case123_0000.nii.gz") + "image_suffix": ".nii.gz", # String after case ID in image filename (e.g., "_0000.nii.gz" for "case123_0000.nii.gz") + "mask_prefix": "", # String before case ID in mask filename + "mask_suffix": ".nii.gz", # String after case ID in mask filename + "labels_map": labels_map_CT, # Dictionary mapping mask values to class labels + }, + { + "image_modality": "MRI", + "image_description": "magnetic resonance imaging (MRI) scan", + "image_folder": "TotalSegmentator-MR/Images", # Directory containing the .nii.gz image files + "mask_folder": "TotalSegmentator-MR/Masks", # Directory containing the mask files + "image_prefix": "", # String before case ID in image filename (e.g., "" for "case123_0000.nii.gz") + "image_suffix": ".nii.gz", # String after case ID in image filename (e.g., "_0000.nii.gz" for "case123_0000.nii.gz") + "mask_prefix": "", # String before case ID in mask filename + "mask_suffix": ".nii.gz", # String after case ID in mask filename + "labels_map": labels_map_MR, # Dictionary mapping mask values to class labels + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/TotalSegmentator/preprocess_segmentation.py b/src/medvision_ds/datasets/TotalSegmentator/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..98cebe7597346c1eeafb08429537ae4946677a23 --- /dev/null +++ b/src/medvision_ds/datasets/TotalSegmentator/preprocess_segmentation.py @@ -0,0 +1,308 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +dataset_info = { + "dataset": "TotalSegmentator", + "dataset_website": "https://github.com/wasserth/TotalSegmentator", + "dataset_data": [ + "https://zenodo.org/records/10047292", # CT + "https://zenodo.org/records/14710732", # MR + ], + "license": ["CC BY 4.0", "CC BY-NC-SA 2.0"], + "paper": ["https://doi.org/10.1148/ryai.230024"], +} + +labels_map_CT = { + "1": "spleen", + "2": "right kidney", + "3": "left kidney", + "4": "gallbladder", + "5": "liver", + "6": "stomach", + "7": "pancreas", + "8": "right adrenal gland", + "9": "left adrenal gland", + "10": "left lung upper lobe", + "11": "left lung lower lobe", + "12": "right lung upper lobe", + "13": "right lung middle lobe", + "14": "right lung lower lobe", + "15": "esophagus", + "16": "trachea", + "17": "thyroid gland", + "18": "small bowel", + "19": "duodenum", + "20": "colon", + "21": "urinary bladder", + "22": "prostate", + "23": "left kidney cyst", + "24": "right kidney cyst", + "25": "sacrum", + "26": "vertebra S1", + "27": "vertebra L5", + "28": "vertebra L4", + "29": "vertebra L3", + "30": "vertebra L2", + "31": "vertebra L1", + "32": "vertebra T12", + "33": "vertebra T11", + "34": "vertebra T10", + "35": "vertebra T9", + "36": "vertebra T8", + "37": "vertebra T7", + "38": "vertebra T6", + "39": "vertebra T5", + "40": "vertebra T4", + "41": "vertebra T3", + "42": "vertebra T2", + "43": "vertebra T1", + "44": "vertebra C7", + "45": "vertebra C6", + "46": "vertebra C5", + "47": "vertebra C4", + "48": "vertebra C3", + "49": "vertebra C2", + "50": "vertebra C1", + "51": "heart", + "52": "aorta", + "53": "pulmonary vein", + "54": "brachiocephalic trunk", + "55": "right subclavian artery", + "56": "left subclavian artery", + "57": "right common carotid artery", + "58": "left common carotid artery", + "59": "left brachiocephalic vein", + "60": "right brachiocephalic vein", + "61": "left atrial appendage", + "62": "superior vena cava", + "63": "inferior vena cava", + "64": "portal vein and splenic vein", + "65": "left iliac artery", + "66": "right iliac artery", + "67": "left iliac vein", + "68": "right iliac vein", + "69": "left humerus", + "70": "right humerus", + "71": "left scapula", + "72": "right scapula", + "73": "left clavicle", + "74": "right clavicle", + "75": "left femur", + "76": "right femur", + "77": "left hip", + "78": "right hip", + "79": "spinal cord", + "80": "left gluteus maximus", + "81": "right gluteus maximus", + "82": "left gluteus medius", + "83": "right gluteus medius", + "84": "left gluteus minimus", + "85": "right gluteus minimus", + "86": "left autochthon", + "87": "right autochthon", + "88": "left iliopsoas", + "89": "right iliopsoas", + "90": "brain", + "91": "skull", + "92": "left 1st rib", + "93": "left 2nd rib", + "94": "left 3rd rib", + "95": "left 4th rib", + "96": "left 5th rib", + "97": "left 6th rib", + "98": "left 7th rib", + "99": "left 8th rib", + "100": "left 9th rib", + "101": "left 10th rib", + "102": "left 11th rib", + "103": "left 12th rib", + "104": "right 1st rib", + "105": "right 2nd rib", + "106": "right 3rd rib", + "107": "right 4th rib", + "108": "right 5th rib", + "109": "right 6th rib", + "110": "right 7th rib", + "111": "right 8th rib", + "112": "right 9th rib", + "113": "right 10th rib", + "114": "right 11th rib", + "115": "right 12th rib", + "116": "sternum", + "117": "costal cartilages", +} + +labels_map_MR = { + "1": "spleen", + "2": "right kidney", + "3": "left kidney", + "4": "gallbladder", + "5": "liver", + "6": "stomach", + "7": "pancreas", + "8": "right adrenal gland", + "9": "left adrenal gland", + "10": "left lung", + "11": "right lung", + "12": "esophagus", + "13": "small bowel", + "14": "duodenum", + "15": "colon", + "16": "urinary bladder", + "17": "prostate", + "18": "sacrum", + "19": "vertebrae", + "20": "intervertebral discs", + "21": "spinal cord", + "22": "heart", + "23": "aorta", + "24": "inferior vena cava", + "25": "portal and splenic veins", + "26": "left iliac artery", + "27": "right iliac artery", + "28": "left iliac vein", + "29": "right iliac vein", + "30": "left humerus", + "31": "right humerus", + "32": "left scapula", + "33": "right scapula", + "34": "left clavicle", + "35": "right clavicle", + "36": "left femur", + "37": "right femur", + "38": "left hip", + "39": "right hip", + "40": "left gluteus maximus", + "41": "right gluteus maximus", + "42": "left gluteus medius", + "43": "right gluteus medius", + "44": "left gluteus minimus", + "45": "right gluteus minimus", + "46": "left autochthon", + "47": "right autochthon", + "48": "left iliopsoas", + "49": "right iliopsoas", + "50": "brain", +} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "computed tomography (CT) scan", + "image_folder": "TotalSegmentator-CT/Images", # Directory containing the .nii.gz image files + "mask_folder": "TotalSegmentator-CT/Masks", # Directory containing the mask files + "image_prefix": "", # String before case ID in image filename (e.g., "" for "case123_0000.nii.gz") + "image_suffix": ".nii.gz", # String after case ID in image filename (e.g., "_0000.nii.gz" for "case123_0000.nii.gz") + "mask_prefix": "", # String before case ID in mask filename + "mask_suffix": ".nii.gz", # String after case ID in mask filename + "labels_map": labels_map_CT, # Dictionary mapping mask values to class labels + }, + { + "image_modality": "MRI", + "image_description": "magnetic resonance imaging (MRI) scan", + "image_folder": "TotalSegmentator-MR/Images", # Directory containing the .nii.gz image files + "mask_folder": "TotalSegmentator-MR/Masks", # Directory containing the mask files + "image_prefix": "", # String before case ID in image filename (e.g., "" for "case123_0000.nii.gz") + "image_suffix": ".nii.gz", # String after case ID in image filename (e.g., "_0000.nii.gz" for "case123_0000.nii.gz") + "mask_prefix": "", # String before case ID in mask filename + "mask_suffix": ".nii.gz", # String after case ID in mask filename + "labels_map": labels_map_MR, # Dictionary mapping mask values to class labels + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/__init__.py b/src/medvision_ds/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..586b08c3f7d1034456667fc92fe67b6a320b9c0c --- /dev/null +++ b/src/medvision_ds/datasets/__init__.py @@ -0,0 +1,50 @@ +"""Datasets in BiometricVQA.""" +from . import ( + AbdomenAtlas__1_0__Mini, + AbdomenCT_1K, + ACDC, + AMOS22, + autoPET_III, + BCV15, + BraTS24, + CAMUS, + Ceph_Biometrics_400, + CrossMoDA, + FLARE22, + FeTA24, + HNTSMRG24, + ISLES24, + KiPA22, + KiTS23, + MSD, + OAIZIB_CM, + SKM_TEA, + ToothFairy2, + TopCoW24, + TotalSegmentator, +) + +__all__ = [ + "AbdomenAtlas__1_0__Mini", + "AbdomenCT_1K", + "ACDC", + "AMOS22", + "autoPET_III", + "BCV15", + "BraTS24", + "CAMUS", + "Ceph_Biometrics_400", + "CrossMoDA", + "FLARE22", + "FeTA24", + "HNTSMRG24", + "ISLES24", + "KiPA22", + "KiTS23", + "MSD", + "OAIZIB_CM", + "SKM_TEA", + "ToothFairy2", + "TopCoW24", + "TotalSegmentator" +] diff --git a/src/medvision_ds/datasets/_dataset_template/__init__.py b/src/medvision_ds/datasets/_dataset_template/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/_dataset_template/download.py b/src/medvision_ds/datasets/_dataset_template/download.py new file mode 100644 index 0000000000000000000000000000000000000000..70271225f6d7fcd41925cd76fce1c9a3ef8b612f --- /dev/null +++ b/src/medvision_ds/datasets/_dataset_template/download.py @@ -0,0 +1,76 @@ +import os +import shutil +import argparse +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: +# Data: +# Format: +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name): + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # .. .. Add your download logic here + # .. .. - Put images in tmp/Images + # .. .. - Put masks in tmp/Masks + + # Move folder to dataset_dir + folders_to_move = [ + "Images", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + # Create dataset directory + dataset_dir = os.path.join(args.dir_datasets_data, args.dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, args.dataset_name) diff --git a/src/medvision_ds/datasets/_dataset_template/preprocess_biometry_type1.py b/src/medvision_ds/datasets/_dataset_template/preprocess_biometry_type1.py new file mode 100644 index 0000000000000000000000000000000000000000..98e63dda192978e9016ba397d5b7bb1bad8b57be --- /dev/null +++ b/src/medvision_ds/datasets/_dataset_template/preprocess_biometry_type1.py @@ -0,0 +1,157 @@ +import os +import argparse +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerBiometry + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "", + "dataset_website": "", + "dataset_data": [""], + "license": [""], + "paper": [""], +} + +landmarks_map = { + "P2": "nasion", + "P5": "subspinale", + "P6": "supramentale", + "P8": "menton", +} + +lines_map = { + "L-2-5": { + "name": "", + "element_keys": ["P2", "P5"], + "element_map_name": "landmarks_map", + }, + "L-2-6": { + "name": "", + "element_keys": ["P2", "P6"], + "element_map_name": "landmarks_map", + }, + "L-2-8": { + "name": "", + "element_keys": ["P2", "P8"], + "element_map_name": "landmarks_map", + }, +} + +angles_map = { + "A-L_2_5-L_2_6": { + "name": "", + "element_keys": ["L-2-5", "L-2-6"], + "element_map_name": "lines_map", + }, +} + +biometrics_map = [ + { + "metric_type": "angle", + "metric_map_name": "angles_map", + "metric_key": "A-L_2_5-L_2_6", + "slice_dim": 0, # 0: sagittal, 1: coronal, 2: axial + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-2-8", + "slice_dim": 0, + }, +] + + +# ------------ +# Task-specific benchmark planning configuration +# ------------ +# - dataset_info: Dictionary containing dataset metadata +# - tasks: List of task configurations where each task contains: +# - image_modality: Type of medical imaging (e.g., "CT", "MRI") +# - image_description: Description of image, used in text prompts +# - image_folder: Directory for .nii.gz image files +# - landmark_folder: Directory for landmark files +# - image_prefix: Filename part before case ID for images +# - image_suffix: Filename part after case ID for images +# - landmark_prefix: Filename part before case ID for landmarks +# - landmark_suffix: Filename part after case ID for landmarks, NOTE: must ends with ".json.gz" or ".json" +# - landmarks_map: Dictionary mapping landmarks to their descriptions +# NOTE: +# - These keys should match the variable names: +# "landmarks_map": landmarks_map, +# "lines_map": lines_map, +# "angles_map": angles_map, +# "biometrics_map": biometrics_map, +# ------------ +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "", + "image_description": "", + "image_folder": "Images", + "landmark_folder": "Landmarks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", # NOTE: must ends with ".json.gz" or ".json" + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + }, + ], +} +# ==================================== + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + args = parser.parse_args() + + # Create dataset directory + dataset_dir = os.path.join(args.dir_datasets_data, args.dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner_segmentation = MedVision_BenchmarkPlannerBiometry( + dataset_dir, + benchmark_plan, + args.dataset_name, + seed=args.random_seed, + split_ratio=args.split_ratio, + ) + planner_segmentation.process() diff --git a/src/medvision_ds/datasets/_dataset_template/preprocess_biometry_type2.py b/src/medvision_ds/datasets/_dataset_template/preprocess_biometry_type2.py new file mode 100644 index 0000000000000000000000000000000000000000..96d8ddade7d7149f0db405aaf09c7c892005b095 --- /dev/null +++ b/src/medvision_ds/datasets/_dataset_template/preprocess_biometry_type2.py @@ -0,0 +1,168 @@ +import os +import argparse +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerBiometry_fromSeg + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +CLUSTER_SIZE_THRESHOLD = 200 + +dataset_info = { + "dataset": "", + "dataset_website": "", + "dataset_data": [""], + "license": [""], + "paper": [""], +} + +labels_map = { + "1": "", +} +# ==================================== + + +# =============== +# DO NOT CHANGE +# =============== +landmarks_map = { + "P1": "most right/anterior/superior endpoint of the major axis", + "P2": "most left/superior/inferior endpoint of the major axis", + "P3": "most right/anterior/superior endpoint of the minor axis", + "P4": "most left/superior/inferior endpoint of the minor axis", +} + +lines_map = { + "L-1-2": { + "name": "marjor axis of the fitted ellipse", + "element_keys": ["P1", "P2"], + "element_map_name": "landmarks_map", + }, + "L-3-4": { + "name": "minor axis of the fitted ellipse", + "element_keys": ["P3", "P4"], + "element_map_name": "landmarks_map", + }, +} + +angles_map = {} + +biometrics_map = [ + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-1-2", + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-3-4", + }, +] +# =============== + + +# ------------ +# Task-specific benchmark planning configuration +# ------------ +# - dataset_info: Dictionary containing dataset metadata +# - tasks: List of task configurations where each task contains: +# - image_modality: Type of medical imaging (e.g., "CT", "MRI") +# - image_description: Description of image, used in text prompts +# - image_folder: Directory for .nii.gz image files +# - landmark_folder: Directory for landmark files +# - image_prefix: Filename part before case ID for images +# - image_suffix: Filename part after case ID for images +# - landmark_prefix: Filename part before case ID for landmarks +# - landmark_suffix: Filename part after case ID for landmarks +# - landmarks_map: Dictionary mapping landmarks to their descriptions +# NOTE: +# - These keys should match the variable names: +# "landmarks_map": landmarks_map, +# "lines_map": lines_map, +# "angles_map": angles_map, +# "biometrics_map": biometrics_map, +# ------------ +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "MRI", + "image_description": "", + "image_folder": "", + "mask_folder": "", + "image_prefix": "", + "landmark_folder": "", + "landmark_figure_folder": "", + "image_suffix": "_T2.nii.gz", + "mask_prefix": "", + "mask_suffix": "_mask.nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 1, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + ], +} +# ==================================== + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + args = parser.parse_args() + + # Create dataset directory + dataset_dir = os.path.join(args.dir_datasets_data, args.dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner_segmentation = MedVision_BenchmarkPlannerBiometry_fromSeg( + dataset_dir, + benchmark_plan, + args.dataset_name, + seed=args.random_seed, + split_ratio=args.split_ratio, + shrunk_bbox_scale=0.9, + enlarged_bbox_scale=1.1, + force_uint16_mask=False, # set to True if the mask files have not been processed + reorient2RAS=False, # set to True if the mask and image files have not been processed + visualization=True, # set to True to visualize the biometric annotations + ) + planner_segmentation.process() diff --git a/src/medvision_ds/datasets/_dataset_template/preprocess_detection.py b/src/medvision_ds/datasets/_dataset_template/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..a65eb15fa7bf562ce6cc2df0ea85f05ebafd408d --- /dev/null +++ b/src/medvision_ds/datasets/_dataset_template/preprocess_detection.py @@ -0,0 +1,103 @@ +import os +import argparse +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "", + "dataset_website": "", + "dataset_data": [""], + "license": [""], + "paper": [""], +} + +labels_map = {} + +# ------------ +# Task-specific benchmark planning configuration +# ------------ +# - dataset_info: Dictionary containing dataset metadata +# - tasks: List of task configurations where each task contains: +# - image_modality: Type of medical imaging (e.g., "CT", "MRI") +# - image_description: Description of image, used in text prompts +# - image_folder: Directory for .nii.gz image files +# - mask_folder: Directory for mask files +# - image_prefix: Filename part before case ID for images +# - image_suffix: Filename part after case ID for images +# - mask_prefix: Filename part before case ID for masks +# - mask_suffix: Filename part after case ID for masks +# - labels_map: Dictionary mapping mask values to class labels +# ------------ +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "", + "image_description": "", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + args = parser.parse_args() + + # Create dataset directory + dataset_dir = os.path.join(args.dir_datasets_data, args.dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner_segmentation = MedVision_BenchmarkPlannerDetection( + dataset_dir, + benchmark_plan, + args.dataset_name, + seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=False, + reorient2RAS=False, + ) + planner_segmentation.process() diff --git a/src/medvision_ds/datasets/_dataset_template/preprocess_segmentation.py b/src/medvision_ds/datasets/_dataset_template/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..0e51097f5fcaf0e80e9b77669251734232319f52 --- /dev/null +++ b/src/medvision_ds/datasets/_dataset_template/preprocess_segmentation.py @@ -0,0 +1,103 @@ +import os +import argparse +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "", + "dataset_website": "", + "dataset_data": [""], + "license": [""], + "paper": [""], +} + +labels_map = {} + +# ------------ +# Task-specific benchmark planning configuration +# ------------ +# - dataset_info: Dictionary containing dataset metadata +# - tasks: List of task configurations where each task contains: +# - image_modality: Type of medical imaging (e.g., "CT", "MRI") +# - image_description: Description of image, used in text prompts +# - image_folder: Directory for .nii.gz image files +# - mask_folder: Directory for mask files +# - image_prefix: Filename part before case ID for images +# - image_suffix: Filename part after case ID for images +# - mask_prefix: Filename part before case ID for masks +# - mask_suffix: Filename part after case ID for masks +# - labels_map: Dictionary mapping mask values to class labels +# ------------ +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "", + "image_description": "", + "image_folder": "Images", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + args = parser.parse_args() + + # Create dataset directory + dataset_dir = os.path.join(args.dir_datasets_data, args.dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner_segmentation = MedVision_BenchmarkPlannerSegmentation( + dataset_dir, + benchmark_plan, + args.dataset_name, + seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=True, + reorient2RAS=True, + ) + planner_segmentation.process() diff --git a/src/medvision_ds/datasets/autoPET_III/__init__.py b/src/medvision_ds/datasets/autoPET_III/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/datasets/autoPET_III/download_fast.py b/src/medvision_ds/datasets/autoPET_III/download_fast.py new file mode 100644 index 0000000000000000000000000000000000000000..3171e1ed0bdfe3f0b8ae4fb0c7b6ca5ec4593302 --- /dev/null +++ b/src/medvision_ds/datasets/autoPET_III/download_fast.py @@ -0,0 +1,102 @@ +import os +import shutil +import argparse +import zipfile +from huggingface_hub import hf_hub_download +from medvision_ds.utils.preprocess_utils import move_folder + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: autoPET-III +# Challenge: https://autopet-iii.grand-challenge.org +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download and extract dataset + for file in ["Images-CT.zip", "Images-PET.zip", "Masks.zip"]: + # Download and extract dataset + hf_hub_download( + repo_id="YongchengYAO/autoPET-III-Lite", + filename=file, + repo_type="dataset", + revision="8bd790fde9b76a48ac6e967ce0e621e8aa4730aa", # commit hash on 2025-02-21 + local_dir=".", + ) + print(f"Extracting {file}... This may take some time.") + with zipfile.ZipFile(file, "r") as zip_ref: + zip_ref.extractall() + os.remove(file) + + # Move folder to dataset_dir + folders_to_move = [ + "Images-CT", + "Images-PET", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/autoPET_III/download_raw.py b/src/medvision_ds/datasets/autoPET_III/download_raw.py new file mode 100644 index 0000000000000000000000000000000000000000..0ccd6cd06643293ee63fa4d279828801beccc960 --- /dev/null +++ b/src/medvision_ds/datasets/autoPET_III/download_raw.py @@ -0,0 +1,166 @@ +import os +import shutil +import argparse +import tarfile +import requests +import numpy as np +import nibabel as nib +from medvision_ds.utils.preprocess_utils import move_folder + + +# ==================================== +# Dataset Info [!] +# ==================================== +# Dataset: autoPET-III +# Challenge: https://autopet-iii.grand-challenge.org +# Format: nii.gz +# ==================================== + + +def download_and_extract(dataset_dir, dataset_name, **kwargs): + """ + Download and extract the AbdomenAtlas dataset. + + NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name + the other arguments must be kwargs + """ + # Download files + current_dir = os.getcwd() + os.chdir(dataset_dir) + tmp_dir = os.path.join(dataset_dir, "tmp") + os.makedirs(tmp_dir, exist_ok=True) + os.chdir(tmp_dir) + print(f"Downloading {dataset_name} dataset to {dataset_dir}...") + + # ==================================== + # Add download logic here [!] + # ==================================== + # Download dataset + url = "https://it-portal.med.uni-muenchen.de/autopet/Autopet_v1.1.tgz" + filename = "Autopet_v1.1.tgz" + print(f"Downloading {url}") + response = requests.get(url, stream=True) + response.raise_for_status() + with open(filename, "wb") as f: + for chunk in response.iter_content(chunk_size=8192): + f.write(chunk) + + # Extract tar archive + try: + with tarfile.open("Autopet_v1.1.tgz", "r:gz") as tar: + tar.extractall() + except tarfile.TarError as e: + print(f"Error extracting archive: {e}") + except FileNotFoundError: + print("Archive file not found") + + # Set write permissions + extracted_dir = "2024-05-10_Autopet_v1.1" + for root, dirs, files in os.walk(extracted_dir): + for d in dirs: + os.chmod(os.path.join(root, d), 0o700) + for f in files: + os.chmod(os.path.join(root, f), 0o600) + + # Move files + shutil.move(os.path.join(extracted_dir, "imagesTr"), ".") + shutil.move(os.path.join(extracted_dir, "labelsTr"), ".") + + # Create directories + os.makedirs("Images-CT", exist_ok=True) + os.makedirs("Images-PET", exist_ok=True) + + # Move files to respective directories + for file in os.listdir("imagesTr"): + if file.endswith("_0000.nii.gz"): + shutil.move(os.path.join("imagesTr", file), os.path.join("Images-CT", file)) + elif file.endswith("_0001.nii.gz"): + shutil.move( + os.path.join("imagesTr", file), os.path.join("Images-PET", file) + ) + os.rename("labelsTr", "Masks") + + # Check and remove empty masks and corresponding images + mask_files = [f for f in os.listdir("Masks") if f.endswith(".nii.gz")] + for mask_file in mask_files: + # Get the ID from mask filename + patient_id = mask_file.replace(".nii.gz", "") + + # Load and check mask + mask_path = os.path.join("Masks", mask_file) + mask_data = nib.load(mask_path).get_fdata() + + # If mask is empty (contains only zeros) + if np.all(mask_data == 0): + print(f"Found empty mask for {patient_id}, removing associated files...") + + # Remove mask file + os.remove(mask_path) + + # Remove corresponding CT image + ct_file = f"{patient_id}_0000.nii.gz" + ct_path = os.path.join("Images-CT", ct_file) + if os.path.exists(ct_path): + print(f"Removing CT image: {ct_path}") + os.remove(ct_path) + + # Remove corresponding PET image + pet_file = f"{patient_id}_0001.nii.gz" + pet_path = os.path.join("Images-PET", pet_file) + if os.path.exists(pet_path): + print(f"Removing PET image: {pet_path}") + os.remove(pet_path) + + # Move folder to dataset_dir + folders_to_move = [ + "Images-CT", + "Images-PET", + "Masks", + ] + for folder in folders_to_move: + move_folder( + os.path.join(tmp_dir, folder), + os.path.join(dataset_dir, folder), + create_dest=True, + ) + # ==================================== + + print(f"Download and extraction completed for {dataset_name}") + os.chdir(dataset_dir) + shutil.rmtree(tmp_dir) + os.chdir(current_dir) + + +def main(dir_datasets_data, dataset_name, **kwargs): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Download and extract dataset + download_and_extract(dataset_dir, dataset_name, **kwargs) + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser(description="Download and extract dataset") + parser.add_argument( + "-d", + "--dir_datasets_data", + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + help="Name of the dataset", + required=True, + ) + args = parser.parse_args() + + main( + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + ) diff --git a/src/medvision_ds/datasets/autoPET_III/preprocess_biometry.py b/src/medvision_ds/datasets/autoPET_III/preprocess_biometry.py new file mode 100644 index 0000000000000000000000000000000000000000..8a146b7a4bc276f53faf806f0552128e670c8097 --- /dev/null +++ b/src/medvision_ds/datasets/autoPET_III/preprocess_biometry.py @@ -0,0 +1,200 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import ( + MedVision_BenchmarkPlannerBiometry_fromSeg, +) + + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# - labels_map +# ==================================== +CLUSTER_SIZE_THRESHOLD = 200 + +dataset_info = { + "dataset": "autoPET-III", + "dataset_website": "https://autopet-iii.grand-challenge.org", + "dataset_data": [ + "https://it-portal.med.uni-muenchen.de/autopet/Autopet_v1.1.tgz", + ], + "license": ["CC-BY-NC 4.0"], + "paper": ["https://doi.org/10.1038/s41597-022-01718-3"], +} + +labels_map = {"1": "tumor"} + +landmarks_map = { + "P1": "most right/anterior/superior endpoint of the major axis", + "P2": "most left/superior/inferior endpoint of the major axis", + "P3": "most right/anterior/superior endpoint of the minor axis", + "P4": "most left/superior/inferior endpoint of the minor axis", +} + +lines_map = { + "L-1-2": { + "name": "marjor axis of the fitted ellipse", + "element_keys": ["P1", "P2"], + "element_map_name": "landmarks_map", + }, + "L-3-4": { + "name": "minor axis of the fitted ellipse", + "element_keys": ["P3", "P4"], + "element_map_name": "landmarks_map", + }, +} + +angles_map = {} + +biometrics_map = [ + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-1-2", + }, + { + "metric_type": "distance", + "metric_map_name": "lines_map", + "metric_key": "L-3-4", + }, +] + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "whole-body computed tomography (CT) scan", + "image_folder": "Images-CT", + "mask_folder": "Masks", + "landmark_folder": "Landmarks-Label1", + "landmark_figure_folder": "Landmarks-Label1-fig", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "landmark_prefix": "", + "landmark_suffix": ".json.gz", + "labels_map": labels_map, + "landmarks_map": landmarks_map, + "lines_map": lines_map, + "angles_map": angles_map, + "biometrics_map": biometrics_map, + "target_label": 1, + "cluster_size_threshold": CLUSTER_SIZE_THRESHOLD, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, # global variable + random_seed=1024, + split_ratio=0.7, + shrunken_bbox_scale=0.9, + enlarged_bbox_scale=1.1, + force_uint16_mask=False, + reorient2RAS=False, + visualization=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerBiometry_fromSeg( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + shrunk_bbox_scale=shrunken_bbox_scale, + enlarged_bbox_scale=enlarged_bbox_scale, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + visualization=visualization, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for biometric measurement task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--shrunken_bbox_scale", + type=float, + default=0.9, + help="Scale factor for shrunken bounding box", + ) + parser.add_argument( + "--enlarged_bbox_scale", + type=float, + default=1.1, + help="Scale factor for enlarged bounding box", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + parser.add_argument( + "--visualization", + action="store_true", + help="Enable visualization of the dataset processing", + ) + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + shrunken_bbox_scale=args.shrunken_bbox_scale, + enlarged_bbox_scale=args.enlarged_bbox_scale, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + visualization=args.visualization, + ) diff --git a/src/medvision_ds/datasets/autoPET_III/preprocess_detection.py b/src/medvision_ds/datasets/autoPET_III/preprocess_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..e94be9e7f9bc9056762898a72d75c6807c4db7e9 --- /dev/null +++ b/src/medvision_ds/datasets/autoPET_III/preprocess_detection.py @@ -0,0 +1,135 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "autoPET-III", + "dataset_website": "https://autopet-iii.grand-challenge.org", + "dataset_data": [ + "https://it-portal.med.uni-muenchen.de/autopet/Autopet_v1.1.tgz", + ], + "license": ["CC-BY-NC 4.0"], + "paper": ["https://doi.org/10.1038/s41597-022-01718-3"], +} + +labels_map = {"1": "tumor"} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "whole-body computed tomography (CT) scan", + "image_folder": "Images-CT", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + { + "image_modality": "PET", + "image_description": "whole-body positron emission tomography (PET) scan", + "image_folder": "Images-PET", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_0001.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, # global variable + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for detection task + planner = MedVision_BenchmarkPlannerDetection( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for detection task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/datasets/autoPET_III/preprocess_segmentation.py b/src/medvision_ds/datasets/autoPET_III/preprocess_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..d04aa2563bd9fc98c1f21ec86724de5f6b5aa92c --- /dev/null +++ b/src/medvision_ds/datasets/autoPET_III/preprocess_segmentation.py @@ -0,0 +1,133 @@ +import os +import argparse +from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus +from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation + +# ==================================== +# Dataset Info [!] +# Do not change keys in +# - benchmark_plan +# ==================================== +dataset_info = { + "dataset": "autoPET-III", + "dataset_website": "https://autopet-iii.grand-challenge.org", + "dataset_data": [ + "https://it-portal.med.uni-muenchen.de/autopet/Autopet_v1.1.tgz", + ], + "license": ["CC-BY-NC 4.0"], + "paper": ["https://doi.org/10.1038/s41597-022-01718-3"], +} + +labels_map = {"1": "tumor"} + +benchmark_plan = { + "dataset_info": dataset_info, + "tasks": [ + { + "image_modality": "CT", + "image_description": "whole-body computed tomography (CT) scan", + "image_folder": "Images-CT", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_0000.nii.gz", + "mask_prefix": "", + "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + { + "image_modality": "PET", + "image_description": "whole-body positron emission tomography (PET) scan", + "image_folder": "Images-PET", + "mask_folder": "Masks", + "image_prefix": "", + "image_suffix": "_0001.nii.gz", "mask_prefix": "", "mask_suffix": ".nii.gz", + "labels_map": labels_map, + }, + ], +} +# ==================================== + + +def main( + dir_datasets_data, + dataset_name, + benchmark_plan=benchmark_plan, # global variable + random_seed=1024, + split_ratio=0.7, + force_uint16_mask=False, + reorient2RAS=False, +): + # Create dataset directory + dataset_dir = os.path.join(dir_datasets_data, dataset_name) + os.makedirs(dataset_dir, exist_ok=True) + + # Change to dataset directory + os.chdir(dataset_dir) + + # Process dataset for segmentation task + planner = MedVision_BenchmarkPlannerSegmentation( + dataset_dir=dataset_dir, + bm_plan=benchmark_plan, + dataset_name=dataset_name, + seed=random_seed, + split_ratio=split_ratio, + force_uint16_mask=force_uint16_mask, + reorient2RAS=reorient2RAS, + num_proc=_get_cgroup_limited_cpus(), + ) + planner.process() + + +if __name__ == "__main__": + # Set up argument parser + parser = argparse.ArgumentParser( + description="Generate benchmark planner for segmentation task." + ) + parser.add_argument( + "-d", + "--dir_datasets_data", + type=str, + help="Directory path where datasets will be stored", + required=True, + ) + parser.add_argument( + "-n", + "--dataset_name", + type=str, + help="Name of the dataset", + required=True, + ) + parser.add_argument( + "--random_seed", + type=int, + default=1024, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--split_ratio", + type=float, + default=0.7, + help="Train/test split ratio (0-1)", + ) + parser.add_argument( + "--force_uint16_mask", + action="store_true", + help="Force mask to be uint16", + ) + parser.add_argument( + "--reorient2RAS", + action="store_true", + help="Reorient images and masks to RAS orientation", + ) + + args = parser.parse_args() + + main( + benchmark_plan=benchmark_plan, # global variable + dir_datasets_data=args.dir_datasets_data, + dataset_name=args.dataset_name, + random_seed=args.random_seed, + split_ratio=args.split_ratio, + force_uint16_mask=args.force_uint16_mask, + reorient2RAS=args.reorient2RAS, + ) diff --git a/src/medvision_ds/utils/__init__.py b/src/medvision_ds/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/medvision_ds/utils/benchmark_planner.py b/src/medvision_ds/utils/benchmark_planner.py new file mode 100644 index 0000000000000000000000000000000000000000..38d98adbc4d69979545c5a6e85a82486266e29f1 --- /dev/null +++ b/src/medvision_ds/utils/benchmark_planner.py @@ -0,0 +1,2150 @@ +import os +import json +import nibabel as nib +import numpy as np +import random +import glob +import sys +import cv2 +import gzip +import matplotlib.pyplot as plt + +from tqdm import tqdm +from scipy.ndimage import label, find_objects +from abc import ABC, abstractmethod +from medvision_ds import __version__ +from medvision_ds.utils.preprocess_utils import ( + convert_to_serializable, +) +from medvision_ds.utils.data_conversion import convert_mask_to_uint16_per_dir, reorient_niigz_RASplus_batch_inplace + + +class MedVision_BenchmarkPlannerBase(ABC): + def __init__( + self, + *, + dataset_dir, + bm_plan, + dataset_name, + seed=1024, + split_ratio=0.7, + num_proc=1, + ): + self.version = __version__ + self.dataset_dir = dataset_dir + self.bm_plan = bm_plan + self.dataset_name = dataset_name + self.seed = seed + self.split_ratio = split_ratio + self.num_proc = num_proc + + @property + @abstractmethod + def task_type(self): + """Abstract property that must return the type of task""" + pass + + @property + @abstractmethod + def bm_plan_file(self): + """Abstract property that must return the benchmark plan file path""" + pass + + def _split_niigz_dataset(self, folder_path): + # Set random seed for reproducibility + random.seed(self.seed) + # Split dataset into training and testing sets + file_list = [ + os.path.basename(f) + for f in glob.glob(os.path.join(folder_path, "*.nii.gz")) + ] + random.shuffle(file_list) + split_idx = int(len(file_list) * self.split_ratio) + train_ls = file_list[:split_idx] + test_ls = file_list[split_idx:] + return train_ls, test_ls + + + def _reorient_niigz_RASplus_batch_inplace(self): + reorient_niigz_RASplus_batch_inplace(self.dataset_dir, workers_limit=self.num_proc) + + def update_tasks_number(self): + self.bm_plan["tasks_number"] = len(self.bm_plan["tasks"]) + print(f"Updated tasks_number to {self.bm_plan['tasks_number']}") + + def reorient_niigz_RASPlus(self): + print( + f"Reorienting images and masks to RAS+ orientation for {self.dataset_name}...\n" + ) + self._reorient_niigz_RASplus_batch_inplace() + + def save_benchmark_plan(self): + print("Saving benchmark plan...\n") + if self.bm_plan_file.endswith(".json.gz"): + with gzip.open(self.bm_plan_file, "wt") as f: + json.dump(self.bm_plan, f, indent=4, default=convert_to_serializable) + else: + with open(self.bm_plan_file, "w") as f: + json.dump(self.bm_plan, f, indent=4, default=convert_to_serializable) + print(f"Benchmark plan saved to {self.bm_plan_file}.\n") + print(f"Dataset preprocessing for {self.dataset_name} completed.\n") + + @abstractmethod + def process_each_task(self): + """ + Placeholder method to be implemented by child classes + + example: + + # Process each task in the benchmark plan + for task_idx, task in enumerate(self.bm_plan["tasks"], 1): + print( + f"{'='*50}\nProcessing {self.task_type} task {task_idx}/{len(self.bm_plan['tasks'])}\n{'='*50}" + ) + # Update task ID + task["task_ID"] = f"{task_idx:02d}" + # Split the dataset into training and testing sets + print("Splitting dataset into training and testing sets...") + imgs_tr, imgs_ts = self._split_niigz_dataset(task["image_folder"]) + print( + f"Split complete: {len(imgs_tr)} training, {len(imgs_ts)} testing cases\n" + ) + # Update the profile of the training and testing sets + print("Updating profiles for training set...\n") + task = self._update_cases_profile(imgs_tr, task, "train") + print("Updating profiles for testing set...\n") + task = self._update_cases_profile(imgs_ts, task, "test") + print(f"Finished processing task {task_idx}\n{'='*50}\n\n") + """ + pass + + @abstractmethod + def process(self): + """ + Placeholder method to be implemented by child classes + + example: + + print(f"Preprocessing {self.dataset_name} dataset in {self.dataset_dir}...\n") + self.update_tasks_number() + if self.reorient2RAS: + self.reorient_niigz_RASPlus() + self.process_each_task() + self.save_benchmark_plan() + """ + pass + + @abstractmethod + def _update_cases_profile(self): + """Placeholder method to be implemented by child classes""" + pass + + +class MedVision_BenchmarkPlanner4SegDetect(MedVision_BenchmarkPlannerBase): + def __init__( + self, + *, + force_uint16_mask=True, + reorient2RAS=True, + **kwargs, + ): + # Call parent class's __init__ + super().__init__(**kwargs) + + # Add additional attributes specific to this class + self.force_uint16_mask = force_uint16_mask + self.reorient2RAS = reorient2RAS + self.mask_folders = self._get_mask_folders() + + def _get_mask_folders(self): + """Get unique mask folders from tasks""" + mask_folders = [] + for task in self.bm_plan["tasks"]: + mask_folders.append(task["mask_folder"]) + return list(set(mask_folders)) + + def _find_labels_map(self, mask_folder): + for task in self.bm_plan["tasks"]: + if task["mask_folder"] == mask_folder: + return task["labels_map"] + + def _match_mask_to_image(self, image_file, task_info): + # Match the mask file with the image file + image_prefix = task_info["image_prefix"] + image_suffix = task_info["image_suffix"] + mask_prefix = task_info["mask_prefix"] + mask_suffix = task_info["mask_suffix"] + image_folder = task_info["image_folder"] + mask_folder = task_info["mask_folder"] + caseID = ( + os.path.basename(image_file) + .replace(image_prefix, "") + .replace(image_suffix, "") + ) + mask_path = f"{mask_folder}/{mask_prefix}{caseID}{mask_suffix}" + image_path = f"{image_folder}/{image_file}" + if not os.path.exists(mask_path): + error_msg = ( + f"\n\nError: Missing mask file for the image {image_path}" + f"Expected mask file: {mask_path}\n" + "Check the 'mask_folder', 'mask_prefix' and 'mask_suffix' in the 'benchmark_plan' dictionary.\n\n" + ) + raise FileNotFoundError(error_msg) + else: + print(f"Found a mask file for {caseID}") + print(f" - Image file: {image_path}") + print(f" - Mask file: {mask_path}\n") + return caseID, image_path, mask_path + + def _check_nii_header_for_img_mask(self, image_file, task_info): + # Match the mask file with the image file + caseID, image_path, mask_path = self._match_mask_to_image(image_file, task_info) + # Inspect the mask files + mask_nii = nib.load(mask_path) + mask_data = mask_nii.get_fdata() + mask_file_info = { + "voxel_size": tuple(round(x, 3) for x in mask_nii.header.get_zooms()), + "affine": np.round(mask_nii.affine, 3), + "orientation": nib.orientations.aff2axcodes(mask_nii.affine), + "array_size": mask_data.shape, + } + # Inspect the image files + img_nii = nib.load(image_path) + img_data = img_nii.get_fdata() + image_file_info = { + "voxel_size": tuple(round(x, 3) for x in img_nii.header.get_zooms()), + "affine": np.round(img_nii.affine, 3), + "orientation": nib.orientations.aff2axcodes(img_nii.affine), + "array_size": img_data.shape, + } + + # NOTE: + # We are not raising an error here, just printing a warning. + # Users can preprocess images and masks to match properties in the download script. + # Check if mask and image properties match + print(f"Checking properties for case: {caseID} ...") + for key in mask_file_info: + if isinstance(mask_file_info[key], np.ndarray): + if not np.allclose( + mask_file_info[key], image_file_info[key], atol=1e-5, rtol=1e-3 + ): + print( + f"\n\nWarning: Mismatch in {key} between image and mask for case {caseID}:\n" + f"Image {key}:\n{image_file_info[key]}\n" + f"Mask {key}:\n{mask_file_info[key]}\n" + ) + elif mask_file_info[key] != image_file_info[key]: + print( + f"\n\nWarning: Mismatch in {key} between image and mask for case {caseID}:\n" + f"Image {key}:\n{image_file_info[key]}\n" + f"Mask {key}:\n{mask_file_info[key]}\n" + ) + print(f"Properties (NIfTI file header) checked!\n") + return ( + caseID, + mask_nii, + mask_data, + image_path, + mask_path, + image_file_info, + mask_file_info, + ) + + def _validate_segmentation_labels_per_dir(self, mask_folder): + labels_map = self._find_labels_map(self, mask_folder) + if not labels_map: + print("\n\nError: labels_map is empty!\n") + sys.exit(1) + # Get list of keys from labels_map + valid_labels = np.array(list(labels_map.keys())) + print(f"Valid labels from labels_map: {valid_labels}") + # Check each .nii.gz file + total_files = sum( + 1 for _ in glob.glob(f"{mask_folder}/**/*.nii.gz", recursive=True) + ) + processed = 0 + for file_path in glob.glob(f"{mask_folder}/**/*.nii.gz", recursive=True): + processed += 1 + print( + f" - [{processed}/{total_files}] Checking: {os.path.basename(file_path)}" + ) + try: + img = nib.load(file_path) + data = img.get_fdata() + unique_vals = np.unique( + data[data != 0] + ) # Get non-zero values directly as numbers + invalid_mask = ~np.isin( + unique_vals, valid_labels.astype(unique_vals.dtype) + ) + invalid_labels = unique_vals[invalid_mask] + if len(invalid_labels) > 0: + print(f"\nError in file: {file_path}") + print(f"Found invalid labels: {invalid_labels}") + print(f"Valid labels are: {valid_labels}") + sys.exit(1) + except Exception as e: + print(f"\nError processing {file_path}: {str(e)}") + sys.exit(1) + + print("\nAll files contain valid labels!") + + def validate_segmentation_labels(self): + print(f"Validating segmentation mask labels for {self.dataset_name}...\n") + for folder in self.mask_folders: + self._validate_segmentation_labels_per_dir(self, folder) + + def convert_masks_to_uint16(self): + print(f"Enforcing integers in masks for {self.dataset_name}...\n") + for folder in self.mask_folders: + mask_folder = os.path.join(self.dataset_dir, folder) + convert_mask_to_uint16_per_dir(mask_folder, workers_limit=self.num_proc) + + def process_each_task(self): + # Process each task in the benchmark plan + for task_idx, task in enumerate(self.bm_plan["tasks"], 1): + print( + f"{'='*50}\nProcessing {self.task_type} task {task_idx}/{len(self.bm_plan['tasks'])}\n{'='*50}" + ) + # Update task ID + task["task_ID"] = f"{task_idx:02d}" + # Split the dataset into training and testing sets + print("Splitting dataset into training and testing sets...") + imgs_tr, imgs_ts = self._split_niigz_dataset(task["image_folder"]) + print( + f"Split complete: {len(imgs_tr)} training, {len(imgs_ts)} testing cases\n" + ) + # Update the profile of the training and testing sets + print("Updating profiles for training set...\n") + self._update_cases_profile(imgs_tr, task, "train") + print("Updating profiles for testing set...\n") + self._update_cases_profile(imgs_ts, task, "test") + print(f"Finished processing task {task_idx}\n{'='*50}\n\n") + + def process(self): + print(f"Preprocessing {self.dataset_name} dataset in {self.dataset_dir}...\n") + self.update_tasks_number() + if self.force_uint16_mask: + self.convert_masks_to_uint16() + if self.reorient2RAS: + self.reorient_niigz_RASPlus() + self.process_each_task() + self.save_benchmark_plan() + + def _update_cases_profile(self): + """Placeholder method to be implemented by child classes""" + pass + + +class MedVision_BenchmarkPlannerSegmentation( + MedVision_BenchmarkPlanner4SegDetect +): + def __init__( + self, + **kwargs, + ): + # Call parent class's __init__ + super().__init__(**kwargs) + + @property + def task_type(self): + return "segmentation" + + @property + def bm_plan_file(self): + return os.path.join( + self.dataset_dir, f"benchmark_plan_segmentation_v{self.version}.json.gz" + ) + + # Only used for getting the file name in Huggingface data loading script + @classmethod + def get_bm_plan_file(cls, dataset_dir, version): + return os.path.join( + dataset_dir, f"benchmark_plan_segmentation_v{version}.json.gz" + ) + + def __inspect_slices(self, profile, idx, slice_vals, unit_area): + mask = slice_vals[0] > 0 + labels = slice_vals[0][mask] + counts = slice_vals[1][mask] + if len(labels) > 0: + slice_profile = [ + { + "label": label, + "pixel_count": int(count), + "ROI_area": count * unit_area, + } + for label, count in zip(labels, counts) + ] + profile.append({"slice_idx": idx, "slice_profile": slice_profile}) + return profile + + def _update_cases_profile(self, images_list, task_info, split): + if split not in ["train", "test"]: + raise ValueError('\n\nError: split should be one of "train" or "test"\n\n') + task_info["task_type"] = self.task_type + task_info[f"{split}_cases_number"] = len(images_list) + for i, img_file in enumerate(images_list, 1): + print( + f"{'-'*50}\n[{i}/{len(images_list)}] Processing: {os.path.basename(img_file)}\n{'-'*50}" + ) + # Check if mask and image properties match + ( + caseID, + mask_nii, + mask_data, + image_path, + mask_path, + image_file_info, + mask_file_info, + ) = self._check_nii_header_for_img_mask(img_file, task_info) + # Find non-zero slices in each dimension + print(f"Updating profile for case: {caseID} ...") + profile_per_slice_x = [] + profile_per_slice_y = [] + profile_per_slice_z = [] + voxel_sizes = mask_nii.header.get_zooms() + # For x dimension + print(" - Inspecting sagittal slices (slices along x-dimension) ...") + unit_area_x = voxel_sizes[1] * voxel_sizes[2] + for i in range(mask_data.shape[0]): + slice_data_x = mask_data[i, :, :] + if slice_data_x.ndim != 2 or 1 in slice_data_x.shape: + print( + f"\n\nWarning: Expected 2D sagittal slice but got shape {slice_data_x.shape} for the {i}-th slice along the x-dimension\n" + ) + else: + slice_vals = np.unique(slice_data_x, return_counts=True) + profile_per_slice_x = self.__inspect_slices( + profile_per_slice_x, i, slice_vals, unit_area_x + ) + # For y dimension + print(" - Inspecting coronal slices (slices along y-dimension) ...") + unit_area_y = voxel_sizes[0] * voxel_sizes[2] + for i in range(mask_data.shape[1]): + slice_data_y = mask_data[:, i, :] + if slice_data_y.ndim != 2 or 1 in slice_data_y.shape: + print( + f"\n\nWarning: Expected 2D coronal slice but got shape {slice_data_y.shape} for the {i}-th slice along the y-dimension\n" + ) + else: + slice_vals = np.unique(slice_data_y, return_counts=True) + profile_per_slice_y = self.__inspect_slices( + profile_per_slice_y, i, slice_vals, unit_area_y + ) + # For z dimension + print(" - Inspecting axial slices (slices along z-dimension) ...") + unit_area_z = voxel_sizes[0] * voxel_sizes[1] + for i in range(mask_data.shape[2]): + slice_data_z = mask_data[:, :, i] + if slice_data_z.ndim != 2 or 1 in slice_data_z.shape: + print( + f"\n\nWarning: Expected 2D axial slice but got shape {slice_data_z.shape} for the {i}-th slice along the z-dimension\n" + ) + else: + slice_vals = np.unique(slice_data_z, return_counts=True) + profile_per_slice_z = self.__inspect_slices( + profile_per_slice_z, i, slice_vals, unit_area_z + ) + # Update the cases profile + if f"{split}_cases" not in task_info: + task_info[f"{split}_cases"] = [] + task_info[f"{split}_cases"].append( + { + "case_ID": caseID, + "image_file": image_path, + "mask_file": mask_path, + "image_file_info": image_file_info, + "mask_file_info": mask_file_info, + "slice_profiles_x": profile_per_slice_x, + "slice_profiles_y": profile_per_slice_y, + "slice_profiles_z": profile_per_slice_z, + } + ) + print(f"\nProfile updated for case {caseID}!\n{'-'*50}\n") + + @staticmethod + def flatten_slice_profiles_2d(cases, slice_dim): + if slice_dim == 0: + slice_profiles_key = "slice_profiles_x" + elif slice_dim == 1: + slice_profiles_key = "slice_profiles_y" + elif slice_dim == 2: + slice_profiles_key = "slice_profiles_z" + else: + raise ValueError(f"\nError: slice_dim should be one of 0, 1, or 2\n") + flatten_slice_profile = [] + for case in cases: + mask_file = case.get("mask_file") + image_file = case.get("image_file") + image_file_info = case.get("image_file_info") + image_size_3d = list(np.uint16(image_file_info.get("array_size"))) + voxel_size = list(image_file_info.get("voxel_size")) + if slice_dim == 0: + image_size_2d = [ + np.uint16(image_size_3d[1]), + np.uint16(image_size_3d[2]), + ] + pixel_size = [voxel_size[1], voxel_size[2]] + elif slice_dim == 1: + image_size_2d = [ + np.uint16(image_size_3d[0]), + np.uint16(image_size_3d[2]), + ] + pixel_size = [voxel_size[0], voxel_size[2]] + elif slice_dim == 2: + image_size_2d = [ + np.uint16(image_size_3d[0]), + np.uint16(image_size_3d[1]), + ] + pixel_size = [voxel_size[0], voxel_size[1]] + slice_profiles_dirX = case.get(slice_profiles_key, []) + for slice_profiles in slice_profiles_dirX: + slice_idx = slice_profiles.get("slice_idx") + slice_profile = slice_profiles.get("slice_profile", []) + for profile in slice_profile: + label = profile.get("label") + pixel_count = profile.get("pixel_count") + roi_area = profile.get("ROI_area") + flatten_slice_profile.append( + { + "image_file": image_file, + "mask_file": mask_file, + "slice_dim": slice_dim, + "slice_idx": slice_idx, + "label": label, + "image_size_2d": image_size_2d, + "pixel_size": pixel_size, + "image_size_3d": image_size_3d, + "voxel_size": voxel_size, + "pixel_count": pixel_count, + "ROI_area": roi_area, + } + ) + return flatten_slice_profile + + +class MedVision_BenchmarkPlannerDetection(MedVision_BenchmarkPlanner4SegDetect): + def __init__( + self, + **kwargs, + ): + # Call parent class's __init__ + super().__init__(**kwargs) + + @property + def task_type(self): + return "detection" + + @property + def bm_plan_file(self): + return os.path.join( + self.dataset_dir, f"benchmark_plan_detection_v{self.version}.json.gz" + ) + + # Only used for getting the file name in Huggingface data loading script + @classmethod + def get_bm_plan_file(cls, dataset_dir, version): + return os.path.join(dataset_dir, f"benchmark_plan_detection_v{version}.json.gz") + + def _find_bounding_boxes_2D(self, binary_mask, pixel_spacing): + """ + Finds 2D bounding boxes for connected components in a binary mask. + Args: + binary_mask (np.ndarray): 2D binary mask array + pixel_spacing (tuple): Physical spacing between pixels (dim1_spacing, dim2_spacing) + Returns: + list[dict]: List of bounding boxes, each containing: + - min_coords: (dim1_min, dim2_min) + - max_coords: (dim1_max, dim2_max) + - center_coords: (dim1_center, dim2_center) + - dimensions: (dim1_length, dim2_length) in pixels + - sizes: (dim1_size, dim2_size) in physical units + Raises: + ValueError: If mask is empty or not 2D + """ + # Input validation + if binary_mask.ndim != 2: + raise ValueError(f"Expected 2D array, got {binary_mask.ndim}D array") + if binary_mask.sum() == 0: + raise ValueError("Empty mask - no objects found") + # Label connected components + labeled_array, num_clusters = label(binary_mask) + bboxes = [] + # Process each cluster + for cluster_id in range(1, num_clusters + 1): + # Create mask for this object + cluster_mask = labeled_array == cluster_id + # Get bounding box using find_objects + slices = find_objects(cluster_mask)[0] + # Extract coordinates + dim1_min, dim1_max = slices[0].start, slices[0].stop - 1 + dim2_min, dim2_max = slices[1].start, slices[1].stop - 1 + # Calculate center coordinates + dim1_center = int((dim1_min + dim1_max) / 2) + dim2_center = int((dim2_min + dim2_max) / 2) + # Calculate dimensions + dim1_length = dim1_max - dim1_min + 1 + dim2_length = dim2_max - dim2_min + 1 + bbox_info = { + "min_coords": (int(dim1_min), int(dim2_min)), + "max_coords": (int(dim1_max), int(dim2_max)), + "center_coords": (dim1_center, dim2_center), + "dimensions": (dim1_length, dim2_length), + "sizes": ( + dim1_length * pixel_spacing[0], + dim2_length * pixel_spacing[1], + ), + "mask_image_ratio": np.sum(cluster_mask) / np.prod(cluster_mask.shape), + } + bboxes.append(bbox_info) + return bboxes + + def _find_bounding_boxes_3D(self, binary_mask, voxel_spacing): + """ + Finds 3D bounding boxes for connected components in a binary mask. + Args: + binary_mask (np.ndarray): 3D binary mask array + voxel_spacing (tuple): Physical spacing between voxels (dim1_spacing, dim2_spacing, dim3_spacing) + Returns: + list[dict]: List of bounding boxes, each containing: + - min_coords: (dim1_min, dim2_min, dim3_min) + - max_coords: (dim1_max, dim2_max, dim3_max) + - center_coords: (dim1_center, dim2_center, dim3_center) + - dimensions: (dim1_length, dim2_length, dim3_length) in voxels + - sizes: (dim1_size, dim2_size, dim3_size) in physical units + Raises: + ValueError: If mask is empty or not 3D + """ + # Input validation + if binary_mask.ndim != 3: + raise ValueError(f"Expected 3D array, got {binary_mask.ndim}D array") + if binary_mask.sum() == 0: + raise ValueError("Empty mask - no non-zero elements found") + # Label connected components + labeled_array, num_clusters = label(binary_mask) + bboxes = [] + # Process each cluster + for cluster_id in range(1, num_clusters + 1): + # Create mask for this cluster + cluster_mask = labeled_array == cluster_id + # Get bounding box using find_objects + obj_info = find_objects(cluster_mask)[0] + # Extract coordinates + dim1_min, dim1_max = obj_info[0].start, obj_info[0].stop - 1 + dim2_min, dim2_max = obj_info[1].start, obj_info[1].stop - 1 + dim3_min, dim3_max = obj_info[2].start, obj_info[2].stop - 1 + # Calculate center coordinates + dim1_center = int((dim1_min + dim1_max) / 2) + dim2_center = int((dim2_min + dim2_max) / 2) + dim3_center = int((dim3_min + dim3_max) / 2) + # Calculate dimensions + dim1_length = dim1_max - dim1_min + 1 + dim2_length = dim2_max - dim2_min + 1 + dim3_length = dim3_max - dim3_min + 1 + bbox_info = { + "min_coords": (int(dim1_min), int(dim2_min), int(dim3_min)), + "max_coords": (int(dim1_max), int(dim2_max), int(dim3_max)), + "center_coords": (dim1_center, dim2_center, dim3_center), + "dimensions": (dim1_length, dim2_length, dim3_length), + "sizes": ( + dim1_length * voxel_spacing[0], + dim2_length * voxel_spacing[1], + dim3_length * voxel_spacing[2], + ), + "mask_image_ratio": np.sum(cluster_mask) / np.prod(cluster_mask.shape), + } + bboxes.append(bbox_info) + return bboxes + + def _inspect_2D_slice(self, profile, idx, mask_2d, pixel_spacing): + slice_vals = np.unique(mask_2d, return_counts=True) + mask = slice_vals[0] > 0 + labels = slice_vals[0][mask] + if len(labels) > 0: + slice_profile = [ + { + "label": label, + "bboxes": self._find_bounding_boxes_2D( + mask_2d == label, pixel_spacing + ), + } + for label in labels + ] + profile.append({"slice_idx": idx, "slice_profile": slice_profile}) + return profile + + def _inspect_3D_image(self, mask_3d, voxel_spacing): + profile_3D = [] + labels = np.unique(mask_3d) + labels = labels[labels != 0] + if len(labels) > 0: + for label in labels: + binary_mask_3d = mask_3d == label + bboxes = self._find_bounding_boxes_3D(binary_mask_3d, voxel_spacing) + profile_3D.append({"label": label, "bboxes": bboxes}) + return profile_3D + + def _update_cases_profile(self, images_list, task_info, split): + if split not in ["train", "test"]: + raise ValueError('\n\nError: split should be one of "train" or "test"\n\n') + task_info["task_type"] = self.task_type + task_info[f"{split}_cases_number"] = len(images_list) + for i, img_file in enumerate(images_list, 1): + print( + f"{'-'*50}\n[{i}/{len(images_list)}] Processing: {os.path.basename(img_file)}\n{'-'*50}" + ) + # Check if mask and image properties match + ( + caseID, + mask_nii, + mask_3d, + image_path, + mask_path, + image_file_info, + mask_file_info, + ) = self._check_nii_header_for_img_mask(img_file, task_info) + print(f"Updating profile for case: {caseID} ...") + voxel_sizes = mask_nii.header.get_zooms() + # Find bounding boxes for 2D slices + print(" - Bounding box inspection for 2D slices") + profile_per_slice_x = [] + profile_per_slice_y = [] + profile_per_slice_z = [] + # For x dimension + print(" - Inspecting sagittal slices (slices along x-dimension) ...") + for i in range(mask_3d.shape[0]): + mask_2d_x = mask_3d[i, :, :] + if mask_2d_x.ndim != 2 or 1 in mask_2d_x.shape: + print( + f"\n\nWarning: Expected 2D sagittal slice but got shape {mask_2d_x.shape} for the {i}-th slice along the x-dimension\n" + ) + else: + profile_per_slice_x = self._inspect_2D_slice( + profile_per_slice_x, + i, + mask_2d_x, + (voxel_sizes[1], voxel_sizes[2]), + ) + # For y dimension + print(" - Inspecting coronal slices (slices along y-dimension) ...") + for i in range(mask_3d.shape[1]): + mask_2d_y = mask_3d[:, i, :] + if mask_2d_y.ndim != 2 or 1 in mask_2d_y.shape: + print( + f"\n\nWarning: Expected 2D coronal slice but got shape {mask_2d_y.shape} for the {i}-th slice along the y-dimension\n" + ) + else: + profile_per_slice_y = self._inspect_2D_slice( + profile_per_slice_y, + i, + mask_2d_y, + (voxel_sizes[0], voxel_sizes[2]), + ) + # For z dimension + print(" - Inspecting axial slices (slices along z-dimension) ...") + for i in range(mask_3d.shape[2]): + mask_2d_z = mask_3d[:, :, i] + if mask_2d_z.ndim != 2 or 1 in mask_2d_z.shape: + print( + f"\n\nWarning: Expected 2D axial slice but got shape {mask_2d_z.shape} for the {i}-th slice along the z-dimension\n" + ) + else: + profile_per_slice_z = self._inspect_2D_slice( + profile_per_slice_z, + i, + mask_2d_z, + (voxel_sizes[0], voxel_sizes[1]), + ) + # Find bounding boxes for 3D objects + print(" - Bounding box inspection for 3D images") + profile_3D = self._inspect_3D_image(mask_3d, voxel_sizes) + # Update the cases profile + if f"{split}_cases" not in task_info: + task_info[f"{split}_cases"] = [] + task_info[f"{split}_cases"].append( + { + "case_ID": caseID, + "image_file": image_path, + "mask_file": mask_path, + "image_file_info": image_file_info, + "mask_file_info": mask_file_info, + "slice_profiles_x": profile_per_slice_x, + "slice_profiles_y": profile_per_slice_y, + "slice_profiles_z": profile_per_slice_z, + "profile_3D": profile_3D, + } + ) + print(f"\nProfile updated for case {caseID}!\n{'-'*50}\n") + + @staticmethod + def flatten_slice_profiles_2d(cases, slice_dim): + if slice_dim == 0: + slice_profiles_key = "slice_profiles_x" + elif slice_dim == 1: + slice_profiles_key = "slice_profiles_y" + elif slice_dim == 2: + slice_profiles_key = "slice_profiles_z" + else: + raise ValueError(f"\nError: slice_dim should be one of 0, 1, or 2\n") + flatten_slice_profile = [] + for case in cases: + mask_file = case.get("mask_file") + image_file = case.get("image_file") + image_file_info = case.get("image_file_info") + image_size_3d = list(np.uint16(image_file_info.get("array_size"))) + voxel_size = list(image_file_info.get("voxel_size")) + if slice_dim == 0: + image_size_2d = [ + np.uint16(image_size_3d[1]), + np.uint16(image_size_3d[2]), + ] + pixel_size = [voxel_size[1], voxel_size[2]] + elif slice_dim == 1: + image_size_2d = [ + np.uint16(image_size_3d[0]), + np.uint16(image_size_3d[2]), + ] + pixel_size = [voxel_size[0], voxel_size[2]] + elif slice_dim == 2: + image_size_2d = [ + np.uint16(image_size_3d[0]), + np.uint16(image_size_3d[1]), + ] + pixel_size = [voxel_size[0], voxel_size[1]] + slice_profiles_dirX = case.get(slice_profiles_key, []) + for slice_profiles in slice_profiles_dirX: + slice_idx = slice_profiles.get("slice_idx") + slice_profile = slice_profiles.get("slice_profile", []) + for profile in slice_profile: + label = profile.get("label") + bboxes = profile.get("bboxes") + flatten_slice_profile.append( + { + "image_file": image_file, + "mask_file": mask_file, + "slice_dim": np.uint8(slice_dim), + "slice_idx": np.uint16(slice_idx), + "label": np.uint16(label), + "image_size_2d": image_size_2d, + "pixel_size": pixel_size, + "image_size_3d": image_size_3d, + "voxel_size": voxel_size, + "bounding_boxes": bboxes, + } + ) + return flatten_slice_profile + + +class MedVision_BenchmarkPlannerBiometry(MedVision_BenchmarkPlannerBase): + def __init__( + self, + **kwargs, + ): + # Call parent class's __init__ + super().__init__(**kwargs) + + @property + def task_type(self): + return "biometry" + + @property + def bm_plan_file(self): + return os.path.join( + self.dataset_dir, f"benchmark_plan_biometry_v{self.version}.json.gz" + ) + + # Only used for getting the file name in Huggingface data loading script + @classmethod + def get_bm_plan_file(cls, dataset_dir, version): + return os.path.join(dataset_dir, f"benchmark_plan_biometry_v{version}.json.gz") + + def _get_img_info(self, image_file, task_info): + # Get the image file path + image_folder = task_info["image_folder"] + image_path = os.path.join(image_folder, image_file) + # Inspect the mask files + img_nii = nib.load(image_path) + img_data = img_nii.get_fdata() + image_file_info = { + "voxel_size": tuple(round(x, 3) for x in img_nii.header.get_zooms()), + "affine": np.round(img_nii.affine, 3), + "orientation": nib.orientations.aff2axcodes(img_nii.affine), + "array_size": img_data.shape, + } + return image_file_info + + def _match_landmark_to_image(self, image_file, task_info): + # Match the mask file with the image file + image_prefix = task_info["image_prefix"] + image_suffix = task_info["image_suffix"] + landmark_prefix = task_info["landmark_prefix"] + landmark_suffix = task_info["landmark_suffix"] + image_folder = task_info["image_folder"] + landmark_folder = task_info["landmark_folder"] + caseID = ( + os.path.basename(image_file) + .replace(image_prefix, "") + .replace(image_suffix, "") + ) + landmark_path = f"{landmark_folder}/{landmark_prefix}{caseID}{landmark_suffix}" + image_path = f"{image_folder}/{image_file}" + if not os.path.exists(landmark_path): + error_msg = ( + f"\n\nError: Missing landmarks file for the image {image_path}\n" + f"Expected landmark file: {landmark_path}\n" + "Check the 'landmark_folder', 'landmark_prefix' and 'landmark_suffix' in the 'benchmark_plan' dictionary.\n\n" + ) + raise FileNotFoundError(error_msg) + else: + print(f"Found a landmark file for {caseID}") + print(f" - Image file: {image_path}") + print(f" - Landmark file: {landmark_path}\n") + return caseID, image_path, landmark_path + + def _check_landmarks_number(self, image_file, task_info): + # Match the landmark file with the image file + caseID, image_path, landmark_path = self._match_landmark_to_image( + image_file, task_info + ) + # Load the landmarks + if landmark_path.endswith(".json.gz"): + with gzip.open(landmark_path, "rt") as f: + landmark_json = json.load(f) + else: # Handle regular .json files + with open(landmark_path, "r") as f: + landmark_json = json.load(f) + # Count landmarks + point_ids = set() + for orientation in landmark_json.keys(): + for slice_data in landmark_json[orientation]: + point_ids.update(slice_data["landmarks"].keys()) + landmarks_num = len(point_ids) + # Check the number of landmarks + if len(task_info["landmarks_map"]) != landmarks_num: + error_msg = ( + f"\n\nError: Number of landmarks in {landmark_path} does not match the expected number.\n" + f"Expected: {len(task_info['landmarks_map'])}, Found: {landmarks_num}\n" + ) + raise ValueError(error_msg) + print(f"Number of landmarks in {landmark_path} matches the expected number.\n") + return caseID, image_path, landmark_path, landmark_json + + def _cal_point2vec(self, point1, point2): + return np.array(point2) - np.array(point1) + + def _cal_vec2acuteAngle(self, vec1, vec2): + return np.arccos( + np.abs(np.dot(vec1, vec2)) / (np.linalg.norm(vec1) * np.linalg.norm(vec2)) + ) + + def _cal_angle( + self, metric_key, metric_map_name, task_info, landmarks, voxel_sizes, slice_dim + ): + # Get 2 lines that form the angle + line1_key = task_info[metric_map_name][metric_key]["element_keys"][0] + line2_key = task_info[metric_map_name][metric_key]["element_keys"][1] + line_map_name = task_info[metric_map_name][metric_key]["element_map_name"] + # Get the points that define the line1 and line2 + point1_line1_key = task_info[line_map_name][line1_key]["element_keys"][0] + point2_line1_key = task_info[line_map_name][line1_key]["element_keys"][1] + point1_line2_key = task_info[line_map_name][line2_key]["element_keys"][0] + point2_line2_key = task_info[line_map_name][line2_key]["element_keys"][1] + point1_line1 = landmarks[point1_line1_key] * np.array(voxel_sizes) + point2_line1 = landmarks[point2_line1_key] * np.array(voxel_sizes) + point1_line2 = landmarks[point1_line2_key] * np.array(voxel_sizes) + point2_line2 = landmarks[point2_line2_key] * np.array(voxel_sizes) + # Calculate the vectors for the line1 and line2 + vec1 = self._cal_point2vec(point1_line1, point2_line1) + vec2 = self._cal_point2vec(point1_line2, point2_line2) + # Calculate the acute angle between the lines + angle = self._cal_vec2acuteAngle(vec1, vec2) + # Get the slice index (where the measurement is made) + slice_idx = landmarks[point1_line1_key][slice_dim] + return np.degrees(angle), slice_idx + + def _cal_distance( + self, metric_key, metric_map_name, task_info, landmarks, voxel_sizes, slice_dim + ): + # Get the points that define the distance + point1_key = task_info[metric_map_name][metric_key]["element_keys"][0] + point2_key = task_info[metric_map_name][metric_key]["element_keys"][1] + point1 = landmarks[point1_key] * np.array(voxel_sizes) + point2 = landmarks[point2_key] * np.array(voxel_sizes) + # Calculate the distance between the points + distance = np.linalg.norm(self._cal_point2vec(point1, point2)) + # Get the slice index (where the measurement is made) + slice_idx = landmarks[point1_key][slice_dim] + return distance, slice_idx + + def _update_cases_profile(self, images_list, task_info, split): + if split not in ["train", "test"]: + raise ValueError('\n\nError: split should be one of "train" or "test"\n\n') + # Updata task type and case number + task_info["task_type"] = self.task_type + task_info[f"{split}_cases_number"] = len(images_list) + for i, img_file in enumerate(images_list, 1): + print( + f"{'-'*50}\n[{i}/{len(images_list)}] Processing: {os.path.basename(img_file)}\n{'-'*50}" + ) + # Find and check the landmarks + caseID, image_path, landmark_path, landmarks_json = ( + self._check_landmarks_number(img_file, task_info) + ) + # Get image file info + image_file_info = self._get_img_info(img_file, task_info) + # Get voxel size + img_nii = nib.load(image_path) + voxel_sizes = img_nii.header.get_zooms() + # Update biometrics for this case + print(f"Updating profile for case: {caseID} ...") + slice_profiles_x = [] + slice_profiles_y = [] + slice_profiles_z = [] + for _, metric in enumerate(task_info["biometrics_map"], 1): + metric_type = metric["metric_type"] + metric_map_name = metric["metric_map_name"] + metric_key = metric["metric_key"] + slice_dim = metric["slice_dim"] + if slice_dim == 0: + slice_landmarks = landmarks_json["slice_landmarks_x"] + elif slice_dim == 1: + slice_landmarks = landmarks_json["slice_landmarks_y"] + elif slice_dim == 2: + slice_landmarks = landmarks_json["slice_landmarks_z"] + # Calculate the metric value + if metric_type == "angle": + # Get the appropriate landmarks for this biometric measurement + line1_key = task_info[metric_map_name][metric_key]["element_keys"][ + 0 + ] + line2_key = task_info[metric_map_name][metric_key]["element_keys"][ + 1 + ] + line_map_name = task_info[metric_map_name][metric_key][ + "element_map_name" + ] + point1_line1_key = task_info[line_map_name][line1_key][ + "element_keys" + ][0] + point2_line1_key = task_info[line_map_name][line1_key][ + "element_keys" + ][1] + point1_line2_key = task_info[line_map_name][line2_key][ + "element_keys" + ][0] + point2_line2_key = task_info[line_map_name][line2_key][ + "element_keys" + ][1] + for i, slice_data in enumerate(slice_landmarks): + if ( + point1_line1_key in slice_data["landmarks"] + and point2_line1_key in slice_data["landmarks"] + and point1_line2_key in slice_data["landmarks"] + and point2_line2_key in slice_data["landmarks"] + ): + landmarks = slice_data["landmarks"] + break + metric_value, slice_idx = self._cal_angle( + metric_key, + metric_map_name, + task_info, + landmarks, + voxel_sizes, + slice_dim, + ) + metric_unit = "degree" + elif metric_type == "distance": + # Get the appropriate landmarks for this biometric measurement + point1_key = task_info[metric_map_name][metric_key]["element_keys"][ + 0 + ] + point2_key = task_info[metric_map_name][metric_key]["element_keys"][ + 1 + ] + for i, slice_data in enumerate(slice_landmarks): + if ( + point1_key in slice_data["landmarks"] + and point2_key in slice_data["landmarks"] + ): + landmarks = slice_data["landmarks"] + break + metric_value, slice_idx = self._cal_distance( + metric_key, + metric_map_name, + task_info, + landmarks, + voxel_sizes, + slice_dim, + ) + metric_unit = "mm" + else: + raise ValueError(f"Invalid metric_type: {metric_type}") + slice_profile = { + "metric_type": metric_type, + "metric_map_name": metric_map_name, + "metric_key": metric_key, + "metric_value": metric_value, + "metric_unit": metric_unit, + "slice_dim": slice_dim, + } + if slice_dim == 0: + slice_profiles_x.append( + { + "slice_idx": slice_idx, + "slice_profile": [slice_profile], + } + ) + elif slice_dim == 1: + slice_profiles_y.append( + { + "slice_idx": slice_idx, + "slice_profile": [slice_profile], + } + ) + elif slice_dim == 2: + slice_profiles_z.append( + { + "slice_idx": slice_idx, + "slice_profile": [slice_profile], + } + ) + else: + raise ValueError("Invalid slice dimension") + # Update the cases profile + if f"{split}_cases" not in task_info: + task_info[f"{split}_cases"] = [] + task_info[f"{split}_cases"].append( + { + "case_ID": caseID, + "image_file": image_path, + "landmark_file": landmark_path, + "image_file_info": image_file_info, + "slice_profiles_x": slice_profiles_x, + "slice_profiles_y": slice_profiles_y, + "slice_profiles_z": slice_profiles_z, + } + ) + print(f"\nProfile updated for case {caseID}!\n{'-'*50}\n") + + @staticmethod + def flatten_slice_profiles_2d(cases, slice_dim): + if slice_dim == 0: + slice_profiles_key = "slice_profiles_x" + elif slice_dim == 1: + slice_profiles_key = "slice_profiles_y" + elif slice_dim == 2: + slice_profiles_key = "slice_profiles_z" + else: + raise ValueError(f"\nError: slice_dim should be one of 0, 1, or 2\n") + flatten_slice_profile = [] + for case in cases: + landmark_file = case.get("landmark_file") + image_file = case.get("image_file") + image_file_info = case.get("image_file_info") + image_size_3d = list(np.uint16(image_file_info.get("array_size"))) + voxel_size = list(image_file_info.get("voxel_size")) + if slice_dim == 0: + image_size_2d = [ + np.uint16(image_size_3d[1]), + np.uint16(image_size_3d[2]), + ] + pixel_size = [voxel_size[1], voxel_size[2]] + elif slice_dim == 1: + image_size_2d = [ + np.uint16(image_size_3d[0]), + np.uint16(image_size_3d[2]), + ] + pixel_size = [voxel_size[0], voxel_size[2]] + elif slice_dim == 2: + image_size_2d = [ + np.uint16(image_size_3d[0]), + np.uint16(image_size_3d[1]), + ] + pixel_size = [voxel_size[0], voxel_size[1]] + slice_profiles_dirX = case.get(slice_profiles_key, []) + for slice_profiles in slice_profiles_dirX: + slice_idx = slice_profiles.get("slice_idx") + slice_profile = slice_profiles.get("slice_profile", []) + for profile in slice_profile: + flatten_slice_profile.append( + { + "image_file": image_file, + "landmark_file": landmark_file, + "slice_dim": slice_dim, + "slice_idx": slice_idx, + "image_size_2d": image_size_2d, + "pixel_size": pixel_size, + "image_size_3d": image_size_3d, + "voxel_size": voxel_size, + "biometric_profile": profile, + } + ) + return flatten_slice_profile + + def process_each_task(self): + # Process each task in the benchmark plan + for task_idx, task in enumerate(self.bm_plan["tasks"], 1): + print( + f"{'='*50}\nProcessing {self.task_type} task {task_idx}/{len(self.bm_plan['tasks'])}\n{'='*50}" + ) + # Update task ID + task["task_ID"] = f"{task_idx:02d}" + # Split the dataset into training and testing sets + print("Splitting dataset into training and testing sets...") + imgs_tr, imgs_ts = self._split_niigz_dataset(task["image_folder"]) + print( + f"Split complete: {len(imgs_tr)} training, {len(imgs_ts)} testing cases\n" + ) + # Update the profile of the training and testing sets + print("Updating profiles for training set...\n") + self._update_cases_profile(imgs_tr, task, "train") + print("Updating profiles for testing set...\n") + self._update_cases_profile(imgs_ts, task, "test") + print(f"Finished processing task {task_idx}\n{'='*50}\n\n") + + def process(self): + print(f"Preprocessing {self.dataset_name} dataset in {self.dataset_dir}...\n") + self.update_tasks_number() + self.process_each_task() + self.save_benchmark_plan() + + +class MedVision_BenchmarkPlannerBiometry_fromSeg( + MedVision_BenchmarkPlanner4SegDetect, MedVision_BenchmarkPlannerBiometry +): + def __init__( + self, + *, + shrunk_bbox_scale=0.9, + enlarged_bbox_scale=1.1, + visualization=False, + **kwargs, + ): + # Call parent class's __init__ + super().__init__(**kwargs) + self.visualization = visualization + self.shrunk_bbox_scale = shrunk_bbox_scale + self.enlarged_bbox_scale = enlarged_bbox_scale + + @property + def task_type(self): + return "biometry" + + @property + def bm_plan_file(self): + return os.path.join( + self.dataset_dir, f"benchmark_plan_biometry_v{self.version}.json.gz" + ) + + # Only used for getting the file name in Huggingface data loading script + @classmethod + def get_bm_plan_file(cls, dataset_dir, version): + return os.path.join(dataset_dir, f"benchmark_plan_biometry_v{version}.json.gz") + + def _match_landmark_to_image_fromSeg(self, image_file, task_info): + # Match the mask file with the image file + caseID = ( + os.path.basename(image_file) + .replace(task_info["image_prefix"], "") + .replace(task_info["image_suffix"], "") + ) + landmark_path = os.path.join( + task_info["landmark_folder"], + f"{task_info['landmark_prefix']}{caseID}{task_info['landmark_suffix']}", + ) + image_path = f"{task_info['image_folder']}/{image_file}" + if not os.path.exists(landmark_path): + error_msg = ( + f"\n\nError: Missing landmarks file for the image {image_path}" + f"Expected landmark file: {landmark_path}\n" + "Check the 'landmark_folder', 'landmark_prefix' and 'landmark_suffix' in the 'benchmark_plan' dictionary.\n\n" + ) + raise FileNotFoundError(error_msg) + else: + print(f"Found a landmark file for {caseID}") + print(f" - Image file: {image_path}") + print(f" - Landmark file: {landmark_path}\n") + return caseID, image_path, landmark_path + + def _get_appropriate_scale(self, pixel_size, img_size, init_scale=10): + """ + Calculate appropriate scale bar size in mm and pixels. + Args: + pixel_sizes (float): Size of one pixel in mm + img_width (int): Image width in pixels + img_height (int): Image height in pixels + init_scale (int): Initial scale in mm (default 10mm) + Returns: + tuple: (scale_mm, scale_pixels) - Selected scale in mm and pixels + """ + scales = [ + 1, + 2, + 5, + 10, + 15, + 20, + 25, + 30, + 40, + 50, + 60, + 70, + 80, + 90, + 100, + ] # Standard scales in mm + # Convert initial scale to pixels + scale_pixels_num = int(init_scale / pixel_size) + # Scale should be between 5% and 25% of smallest image dimension + min_pixels = img_size * 0.05 + max_pixels = img_size * 0.25 + if scale_pixels_num < min_pixels: + # Find next larger scale + for scale in scales: + if scale > init_scale: + return self._get_appropriate_scale(pixel_size, img_size, scale) + elif scale_pixels_num > max_pixels: + # Find next smaller scale + for scale in reversed(scales): + if scale < init_scale: + return self._get_appropriate_scale(pixel_size, img_size, scale) + return init_scale, scale_pixels_num + + def _find_scaled_bounding_boxes_2D(self, binary_mask, scale): + # Input validation + if binary_mask.ndim != 2: + raise ValueError(f"Expected 2D array, got {binary_mask.ndim}D array") + if binary_mask.sum() == 0: + raise ValueError("Empty mask - no objects found") + if scale <= 0: + raise ValueError(f"Invalid scale value: {scale}. It must be positive.") + # Label connected components + labeled_array, num_objects = label(binary_mask) + bboxes = [] + # Process each object + for object_id in range(1, num_objects + 1): + # Create mask for this object + object_mask = labeled_array == object_id + # Get bounding box using find_objects + slices = find_objects(object_mask)[0] + # Get original bounding box coordinates + dim0_min, dim0_max = slices[0].start, slices[0].stop - 1 + dim1_min, dim1_max = slices[1].start, slices[1].stop - 1 + # Calculate center coordinates + dim0_center = (dim0_min + dim0_max) / 2 + dim1_center = (dim1_min + dim1_max) / 2 + # Calculate original dimensions + dim0_length = dim0_max - dim0_min + 1 + dim1_length = dim1_max - dim1_min + 1 + # Calculate enlarged dimensions + dim0_length_scaled = int(dim0_length * scale) + dim1_length_scaled = int(dim1_length * scale) + # Calculate new min/max coordinates while keeping center fixed + dim0_min_scaled = int(dim0_center - dim0_length_scaled / 2) + dim0_max_scaled = int(dim0_center + dim0_length_scaled / 2) + dim1_min_scaled = int(dim1_center - dim1_length_scaled / 2) + dim1_max_scaled = int(dim1_center + dim1_length_scaled / 2) + # Clip to image boundaries + dim0_min_scaled = max(0, dim0_min_scaled) + dim0_max_scaled = min(binary_mask.shape[0] - 1, dim0_max_scaled) + dim1_min_scaled = max(0, dim1_min_scaled) + dim1_max_scaled = min(binary_mask.shape[1] - 1, dim1_max_scaled) + bbox_info = { + "min_coords": (int(dim0_min_scaled), int(dim1_min_scaled)), + "max_coords": (int(dim0_max_scaled), int(dim1_max_scaled)), + } + bboxes.append(bbox_info) + return bboxes + + def __fit_ellipses( + self, mask_2d, cluster_size_threshold, pixel_sizes, slice_dim, slice_idx + ): + # Find connected components and store them with sizes + labeled_array, _ = label(mask_2d) + sizes = np.bincount(labeled_array.ravel())[1:] + # Store visualization info + valid_ellipses = [] + valid_centers = [] + valid_axes = [] + valid_angles = [] + valid_landmarks_coords = [] + valid_ROIs = [] + # Sort clusters by size (largest to smallest) + sorted_cluster_indices = np.argsort(-sizes) # Negative for descending order + # Loop through all clusters + landmarks = [] + for cluster_idx in sorted_cluster_indices: + cluster_label = cluster_idx + 1 + cluster_size = sizes[cluster_label - 1] + if cluster_size < cluster_size_threshold: + continue + # Get mask for current cluster + mask_1ROI = (labeled_array == cluster_label).astype(np.uint8) + # Fit ellipse to current cluster + contours, _ = cv2.findContours( + mask_1ROI, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE + ) + # Convert contour points to real-world coordinates + contour_real = contours[0].squeeze() * pixel_sizes + # Fit ellipse in real-world coordinates + ellipse_real = cv2.fitEllipse(contour_real.astype(np.float32)) + center_real, axes_real, angle = ellipse_real + # Convert center back to pixel coordinates + center = ( + center_real[0] / pixel_sizes[0], + center_real[1] / pixel_sizes[1], + ) + # Convert axes back to pixel coordinates while preserving aspect ratio + axes = ( + axes_real[0] / pixel_sizes[0], + axes_real[1] / pixel_sizes[1], + ) + # Calculate ellipse points in pixel coordinates + angle_rad = np.deg2rad(angle) + a, b = axes[0] / 2, axes[1] / 2 + major_x = a * np.cos(angle_rad) + major_y = a * np.sin(angle_rad) + minor_x = -b * np.sin(angle_rad) + minor_y = b * np.cos(angle_rad) + # Calculate landmark coordinates in pixel space + idx1_dim1 = center[0] + major_x + idx1_dim0 = center[1] + major_y + idx2_dim1 = center[0] - major_x + idx2_dim0 = center[1] - major_y + idx3_dim1 = center[0] + minor_x + idx3_dim0 = center[1] + minor_y + idx4_dim1 = center[0] - minor_x + idx4_dim0 = center[1] - minor_y + # Calculate axis lengths + p1p2_length = np.sqrt( + (idx1_dim0 - idx2_dim0) ** 2 + (idx1_dim1 - idx2_dim1) ** 2 + ) + p3p4_length = np.sqrt( + (idx3_dim0 - idx4_dim0) ** 2 + (idx3_dim1 - idx4_dim1) ** 2 + ) + # Skip if either axis has zero length + if p1p2_length < 1e-6 or p3p4_length < 1e-6: + continue + # Swap points if needed for consistency + if p1p2_length < p3p4_length: + idx1_dim0, idx3_dim0 = idx3_dim0, idx1_dim0 + idx1_dim1, idx3_dim1 = idx3_dim1, idx1_dim1 + idx2_dim0, idx4_dim0 = idx4_dim0, idx2_dim0 + idx2_dim1, idx4_dim1 = idx4_dim1, idx2_dim1 + # Reorder points based on index values + if (idx1_dim0 < idx2_dim0) or ( + idx1_dim0 == idx2_dim0 and idx1_dim1 < idx2_dim1 + ): + idx1_dim0, idx2_dim0 = idx2_dim0, idx1_dim0 + idx1_dim1, idx2_dim1 = idx2_dim1, idx1_dim1 + + if (idx3_dim0 < idx4_dim0) or ( + idx3_dim0 == idx4_dim0 and idx3_dim1 < idx4_dim1 + ): + idx3_dim0, idx4_dim0 = idx4_dim0, idx3_dim0 + idx3_dim1, idx4_dim1 = idx4_dim1, idx3_dim1 + + # Get bounding boxes and check if landmarks are within + enlarged_bboxes = self._find_scaled_bounding_boxes_2D( + mask_1ROI, self.enlarged_bbox_scale + ) + shrunk_bboxes = self._find_scaled_bounding_boxes_2D( + mask_1ROI, self.shrunk_bbox_scale + ) + enlarged_min, enlarged_max = ( + enlarged_bboxes[0]["min_coords"], + enlarged_bboxes[0]["max_coords"], + ) + shrunk_min, shrunk_max = ( + shrunk_bboxes[0]["min_coords"], + shrunk_bboxes[0]["max_coords"], + ) + # Check if all landmarks are within the buffer zone between shrunk and enlarged boxes + points = [ + (idx1_dim0, idx1_dim1), + (idx2_dim0, idx2_dim1), + (idx3_dim0, idx3_dim1), + (idx4_dim0, idx4_dim1), + ] + all_within = all( + enlarged_min[0] <= p[0] <= enlarged_max[0] + and enlarged_min[1] <= p[1] <= enlarged_max[1] + and ( + p[0] <= shrunk_min[0] + or p[0] >= shrunk_max[0] + or p[1] <= shrunk_min[1] + or p[1] >= shrunk_max[1] + ) + for p in points + ) + if all_within: + # Create landmark dictionary + landmark_dict = {} + if slice_dim == 0: + landmark_dict = { + "P1": [ + int(slice_idx), + int(round(idx1_dim0)), + int(round(idx1_dim1)), + ], + "P2": [ + int(slice_idx), + int(round(idx2_dim0)), + int(round(idx2_dim1)), + ], + "P3": [ + int(slice_idx), + int(round(idx3_dim0)), + int(round(idx3_dim1)), + ], + "P4": [ + int(slice_idx), + int(round(idx4_dim0)), + int(round(idx4_dim1)), + ], + "ROI_pixels_count": int(cluster_size), + } + elif slice_dim == 1: + landmark_dict = { + "P1": [ + int(round(idx1_dim0)), + int(slice_idx), + int(round(idx1_dim1)), + ], + "P2": [ + int(round(idx2_dim0)), + int(slice_idx), + int(round(idx2_dim1)), + ], + "P3": [ + int(round(idx3_dim0)), + int(slice_idx), + int(round(idx3_dim1)), + ], + "P4": [ + int(round(idx4_dim0)), + int(slice_idx), + int(round(idx4_dim1)), + ], + "ROI_pixels_count": int(cluster_size), + } + else: + landmark_dict = { + "P1": [ + int(round(idx1_dim0)), + int(round(idx1_dim1)), + int(slice_idx), + ], + "P2": [ + int(round(idx2_dim0)), + int(round(idx2_dim1)), + int(slice_idx), + ], + "P3": [ + int(round(idx3_dim0)), + int(round(idx3_dim1)), + int(slice_idx), + ], + "P4": [ + int(round(idx4_dim0)), + int(round(idx4_dim1)), + int(slice_idx), + ], + "ROI_pixels_count": int(cluster_size), + } + landmarks.append(landmark_dict) + # Store visualization info + valid_ellipses.append(ellipse_real) + valid_centers.append(center) + valid_axes.append(axes) + valid_angles.append(angle) + valid_landmarks_coords.append(points) + valid_ROIs.append(mask_1ROI) + valid_ellipses_info = { + "ellipses": valid_ellipses, + "centers": valid_centers, + "axes": valid_axes, + "angles": valid_angles, + "landmarks_coords": valid_landmarks_coords, + "ROIs": valid_ROIs, + } + return landmarks, valid_ellipses_info + + def __plot_img_ellipse_landmarks( + self, + image_2d, + pixel_sizes, + valid_ellipses_info, + slice_dim, + slice_idx, + case_id, + landmarks_fig_dir, + ): + # Extract ellipse information + valid_ellipses = valid_ellipses_info["ellipses"] + valid_centers = valid_ellipses_info["centers"] + valid_axes = valid_ellipses_info["axes"] + valid_angles = valid_ellipses_info["angles"] + valid_ROIs = valid_ellipses_info["ROIs"] + valid_landmarks_coords = valid_ellipses_info["landmarks_coords"] + colors = [ + "#4285F4", + "#EA4335", + "#FDB813", + "#34A853", + ] + + # Create visualization + img_height, img_width = image_2d.shape + aspect_ratio = img_width / img_height + base_size = 10 + figsize = ( + (base_size * aspect_ratio, base_size) + if aspect_ratio > 1 + else (base_size, base_size / aspect_ratio) + ) + # Calculate aspect ratio based on pixel sizes + aspect_ratio = pixel_sizes[1] / pixel_sizes[0] + # Plot image and landmarks with correct aspect ratio + plt.figure(figsize=figsize) + plt.imshow( + image_2d.T, + cmap="gray", + origin="lower", + aspect=aspect_ratio, + zorder=-1, + ) + + # Plot all valid ellipses and landmarks + for i in range(len(valid_ellipses)): + # Add ellipse + ellipse_patch = plt.matplotlib.patches.Ellipse( + xy=(valid_centers[i][1], valid_centers[i][0]), + width=valid_axes[i][1], + height=valid_axes[i][0], + angle=-valid_angles[i], + fill=False, + color="red", + linewidth=2, + zorder=1, + ) + plt.gca().add_patch(ellipse_patch) + # Plot mask contour + plt.contour( + valid_ROIs[i].T, + levels=[0.5], + colors="#97D540", + linewidths=2, + origin="lower", + zorder=0, + ) + # Plot landmarks + for j, (x, y) in enumerate(valid_landmarks_coords[i]): + plt.scatter( + x, + y, + color=colors[j], + edgecolors="black", + marker="o", + s=60, + linewidth=1, + label=f"P{j+1}", + zorder=2, + ) + # Plot axes + plt.plot( + [ + valid_landmarks_coords[i][0][0], + valid_landmarks_coords[i][1][0], + ], + [ + valid_landmarks_coords[i][0][1], + valid_landmarks_coords[i][1][1], + ], + color="#F37020", + linestyle="-", + linewidth=2, + label="major axis", + zorder=3, + ) + plt.plot( + [ + valid_landmarks_coords[i][2][0], + valid_landmarks_coords[i][3][0], + ], + [ + valid_landmarks_coords[i][2][1], + valid_landmarks_coords[i][3][1], + ], + color="#FBBC05", + linestyle="-", + linewidth=2, + label="minor axis", + zorder=3, + ) + # Add scale bar + min_idx = np.argmin(image_2d.shape[:2]) + scale_mm, num_pixels_dim_min = self._get_appropriate_scale( + pixel_sizes[min_idx], + image_2d.shape[min_idx], + init_scale=10, + ) + num_pixels_dim_max = int(scale_mm / pixel_sizes[1 - min_idx]) + if min_idx == 0: + scale_pixels_dim0, scale_pixels_dim1 = ( + num_pixels_dim_min, + num_pixels_dim_max, + ) + else: + scale_pixels_dim0, scale_pixels_dim1 = ( + num_pixels_dim_max, + num_pixels_dim_min, + ) + start_x, start_y = int(img_height * 0.05), int(img_width * 0.05) + end_x, end_y = ( + start_x + scale_pixels_dim0, + start_y + scale_pixels_dim1, + ) + plt.plot([start_x, end_x], [start_y, start_y], "w-", linewidth=2) + plt.plot([start_x, start_x], [start_y, end_y], "w-", linewidth=2) + plt.text( + end_x + img_height * 0.01, + start_y, + f"{scale_mm} mm", + color="white", + horizontalalignment="left", + ) + # Set title and labels + if slice_dim == 0: + slice_filename = f"Sagittal_{slice_idx}.png" + plt.xlabel("Anterior →", fontsize=14) + plt.ylabel("Superior →", fontsize=14) + elif slice_dim == 1: + slice_filename = f"Coronal_{slice_idx}.png" + plt.xlabel("Right →", fontsize=14) + plt.ylabel("Superior →", fontsize=14) + else: + slice_filename = f"Axial_{slice_idx}.png" + plt.xlabel("Right →", fontsize=14) + plt.ylabel("Anterior →", fontsize=14) + plt.tight_layout(pad=1.5, rect=[0.05, 0.05, 0.95, 0.95]) + # Save visualization + case_fig_dir = os.path.join(landmarks_fig_dir, case_id) + os.makedirs(case_fig_dir, exist_ok=True) + plt.savefig( + os.path.join(case_fig_dir, slice_filename), + bbox_inches="tight", + ) + plt.close() + + def _extract_ellipse_landmarks(self, task_info): + """Extract ellipse landmarks from binary masks and save them as JSON files with visualizations. + Logic: + 1. Load mask and image data, extract case ID + 2. Process each dimension (sagittal, coronal, axial) + 3. For each slice: find connected components, fit ellipse, calculate landmarks + 4. Generate visualization with landmarks and scale bars + 5. Save landmarks to JSON and visualizations to PNG files + """ + mask_prefix = task_info["mask_prefix"] + mask_suffix = task_info["mask_suffix"] + image_prefix = task_info["image_prefix"] + image_suffix = task_info["image_suffix"] + landmark_prefix = task_info["landmark_prefix"] + landmark_suffix = task_info["landmark_suffix"] + img_dir = task_info["image_folder"] + mask_dir = task_info["mask_folder"] + target_label = task_info["target_label"] + cluster_size_threshold = task_info["cluster_size_threshold"] + landmarks_json_dir = task_info["landmark_folder"] + landmarks_fig_dir = task_info["landmark_figure_folder"] + + mask_files = glob.glob(os.path.join(mask_dir, "*.nii.gz")) + print(f"Found {len(mask_files)} mask files") + # Process each mask file + for i, mask_file in enumerate(mask_files, 1): + case_id = ( + os.path.basename(mask_file) + .replace(mask_prefix, "") + .replace(mask_suffix, "") + ) + print( + f"\n[{i}/{len(mask_files)}] Processing: {case_id}...\nMask file: {mask_file}" + ) + # Load mask and image data + mask_nii = nib.load(mask_file) + mask_data = mask_nii.get_fdata() + mask_binary = (mask_data == target_label).astype(np.uint8) + image_file = os.path.join( + img_dir, + f"{image_prefix}{case_id}{image_suffix}", + ) + image_nii = nib.load(image_file) + image_data = image_nii.get_fdata() + voxel_sizes = image_nii.header.get_zooms() + # Initialize landmark storage + slice_landmarks_x, slice_landmarks_y, slice_landmarks_z = [], [], [] + # Process each dimension + for slice_dim in range(3): + print(f" - Processing dimension {slice_dim}...") + n_slices = mask_binary.shape[slice_dim] + voxel_array = np.array(voxel_sizes) + if slice_dim == 0: + pixel_sizes = voxel_array[[1, 2]] + elif slice_dim == 1: + pixel_sizes = voxel_array[[0, 2]] + else: + pixel_sizes = voxel_array[[0, 1]] + # Process each slice + for slice_idx in tqdm( + range(n_slices), desc=f" -- Processing slices (dim{slice_dim})" + ): + # Extract 2D slice based on dimension + if slice_dim == 0: + mask_2d, image_2d = ( + mask_binary[slice_idx, :, :], + image_data[slice_idx, :, :], + ) + elif slice_dim == 1: + mask_2d, image_2d = ( + mask_binary[:, slice_idx, :], + image_data[:, slice_idx, :], + ) + else: + mask_2d, image_2d = ( + mask_binary[:, :, slice_idx], + image_data[:, :, slice_idx], + ) + if not np.any(mask_2d): + continue + # Fit ellipses + landmarks, valid_ellipses_info = self.__fit_ellipses( + mask_2d, + cluster_size_threshold, + pixel_sizes, + slice_dim, + slice_idx, + ) + # Visualize landmarks and save to file + if self.visualization and len(valid_ellipses_info["ellipses"]) > 0: + self.__plot_img_ellipse_landmarks( + image_2d, + pixel_sizes, + valid_ellipses_info, + slice_dim, + slice_idx, + case_id, + landmarks_fig_dir, + ) + # Store landmarks for current slice if any valid ellipses were found + if len(landmarks) > 0: + slice_dict = { + "slice_idx": int(slice_idx), + "landmarks": landmarks, + } + if slice_dim == 0: + slice_landmarks_x.append(slice_dict) + elif slice_dim == 1: + slice_landmarks_y.append(slice_dict) + else: + slice_landmarks_z.append(slice_dict) + # Save all landmarks to JSON + final_dict = { + "slice_landmarks_x": slice_landmarks_x, + "slice_landmarks_y": slice_landmarks_y, + "slice_landmarks_z": slice_landmarks_z, + } + ( + os.makedirs(landmarks_json_dir) + if not os.path.exists(landmarks_json_dir) + else None + ) + output_file = os.path.join( + landmarks_json_dir, f"{landmark_prefix}{case_id}{landmark_suffix}" + ) + # Check if output file ends with .json.gz or .json + if output_file.endswith(".json.gz"): + with gzip.open(output_file, "wt") as f: + json.dump(final_dict, f, indent=4) + else: + with open(output_file, "w") as f: + json.dump(final_dict, f, indent=4) + print(f"Saved landmarks to {output_file}") + + def _get_biometrics(self, task_info, landmarks, voxel_sizes, slice_dim): + biometrics = [] + for _, metric in enumerate(task_info["biometrics_map"], 1): + metric_type = metric["metric_type"] + metric_map_name = metric["metric_map_name"] + metric_key = metric["metric_key"] + if metric_type == "angle": + metric_value, _ = self._cal_angle( + metric_key, + metric_map_name, + task_info, + landmarks, + voxel_sizes, + slice_dim, + ) + metric_unit = "degree" + elif metric_type == "distance": + metric_value, _ = self._cal_distance( + metric_key, + metric_map_name, + task_info, + landmarks, + voxel_sizes, + slice_dim, + ) + metric_unit = "mm" + else: + raise ValueError(f"Invalid metric_type: {metric_type}") + biometrics.append( + { + "metric_type": metric_type, + "metric_map_name": metric_map_name, + "metric_key": metric_key, + "metric_value": metric_value, + "metric_unit": metric_unit, + "slice_dim": slice_dim, + } + ) + return biometrics + + def _get_biometrics_batch(self, task_info, landmarks_json, voxel_sizes, slice_dim): + slice_profiles = [] + if slice_dim == 0: + slice_landmarks = landmarks_json["slice_landmarks_x"] + elif slice_dim == 1: + slice_landmarks = landmarks_json["slice_landmarks_y"] + elif slice_dim == 2: + slice_landmarks = landmarks_json["slice_landmarks_z"] + else: + raise ValueError("Invalid slice dimension") + for _, slice_landmark in enumerate(slice_landmarks, 1): + landmarks_list = slice_landmark["landmarks"] + slice_profile = [] + for landmarks in landmarks_list: + slice_idx = slice_landmark["slice_idx"] + slice_profile.append( + self._get_biometrics(task_info, landmarks, voxel_sizes, slice_dim) + ) + slice_profiles.append( + { + "slice_idx": slice_idx, + "slice_profile": slice_profile, + } + ) + return slice_profiles + + def _update_cases_profile(self, images_list, task_info, split): + if split not in ["train", "test"]: + raise ValueError('\n\nError: split should be one of "train" or "test"\n\n') + task_info["task_type"] = self.task_type + task_info[f"{split}_cases_number"] = len(images_list) + # Fit an ellipse to the chosen ROI and get 4 landmarks on the ellipse + self._extract_ellipse_landmarks(task_info) + for i, img_file in enumerate(images_list, 1): + print( + f"{'-'*50}\n[{i}/{len(images_list)}] Processing: {os.path.basename(img_file)}\n{'-'*50}" + ) + # Check if mask and image properties match + ( + caseID, + mask_nii, + _, + image_path, + mask_path, + image_file_info, + mask_file_info, + ) = self._check_nii_header_for_img_mask(img_file, task_info) + # Find and check the landmarks + _, _, landmark_path = self._match_landmark_to_image_fromSeg( + img_file, task_info + ) + # Load landmarks from either .json.gz or .json file + if landmark_path.endswith(".json.gz"): + with gzip.open(landmark_path, "rt") as f: + landmarks_json = json.load(f) + else: + with open(landmark_path, "r") as f: + landmarks_json = json.load(f) + # Get voxel size + voxel_sizes = mask_nii.header.get_zooms() + # Update biometrics for this case + print(f"Updating profile for case: {caseID} ...") + # Generate profile for sagittal, coronal and axial slices + if len(landmarks_json["slice_landmarks_x"]) > 0: + print(" - Generating profile for sagittal slices...") + slice_profiles_x = self._get_biometrics_batch( + task_info, landmarks_json, voxel_sizes, slice_dim=0 + ) + else: + slice_profiles_x = [] + if len(landmarks_json["slice_landmarks_y"]) > 0: + print(" - Generating profile for coronal slices...") + slice_profiles_y = self._get_biometrics_batch( + task_info, landmarks_json, voxel_sizes, slice_dim=1 + ) + else: + slice_profiles_y = [] + if len(landmarks_json["slice_landmarks_z"]) > 0: + print(" - Generating profile for axial slices...") + slice_profiles_z = self._get_biometrics_batch( + task_info, landmarks_json, voxel_sizes, slice_dim=2 + ) + else: + slice_profiles_z = [] + # Update the cases profile + if f"{split}_cases" not in task_info: + task_info[f"{split}_cases"] = [] + task_info[f"{split}_cases"].append( + { + "case_ID": caseID, + "image_file": image_path, + "landmark_file": landmark_path, + "mask_file": mask_path, + "image_file_info": image_file_info, + "mask_file_info": mask_file_info, + "slice_profiles_x": slice_profiles_x, + "slice_profiles_y": slice_profiles_y, + "slice_profiles_z": slice_profiles_z, + } + ) + print(f"\nProfile updated for case {caseID}!\n{'-'*50}\n") + + @staticmethod + def flatten_slice_profiles_2d(cases, slice_dim): + if slice_dim == 0: + slice_profiles_key = "slice_profiles_x" + elif slice_dim == 1: + slice_profiles_key = "slice_profiles_y" + elif slice_dim == 2: + slice_profiles_key = "slice_profiles_z" + else: + raise ValueError(f"\nError: slice_dim should be one of 0, 1, or 2\n") + flatten_slice_profile = [] + for case in cases: + landmark_file = case.get("landmark_file") + image_file = case.get("image_file") + mask_file = case.get("mask_file") + image_file_info = case.get("image_file_info") + image_size_3d = list(np.uint16(image_file_info.get("array_size"))) + voxel_size = list(image_file_info.get("voxel_size")) + if slice_dim == 0: + image_size_2d = [ + np.uint16(image_size_3d[1]), + np.uint16(image_size_3d[2]), + ] + pixel_size = [voxel_size[1], voxel_size[2]] + elif slice_dim == 1: + image_size_2d = [ + np.uint16(image_size_3d[0]), + np.uint16(image_size_3d[2]), + ] + pixel_size = [voxel_size[0], voxel_size[2]] + elif slice_dim == 2: + image_size_2d = [ + np.uint16(image_size_3d[0]), + np.uint16(image_size_3d[1]), + ] + pixel_size = [voxel_size[0], voxel_size[1]] + # For the "Tumor-Lesion-Size" task, slice_profiles_dirX could be empty + slice_profiles_dirX_ls = case.get(slice_profiles_key, []) + if len(slice_profiles_dirX_ls) == 0: + continue + else: + for slice_profiles_dict in slice_profiles_dirX_ls: + slice_idx = slice_profiles_dict.get("slice_idx") + slice_profiles_ls = slice_profiles_dict.get("slice_profile") + + # For the "Tumor-Lesion-Size" task, slice_profiles is a list of profiles for each ellipse + # - Each ellipse profile is a list of dictionaries + # - Each dictionary contains the slice_idx and the biometric profile for the major and minor axes + biometric_ellipses_ls = [] + metric_value_major_axis = None + metric_value_minor_axis = None + for ellipse_profile_ls in slice_profiles_ls: + for axis_profile_dict in ellipse_profile_ls: + if axis_profile_dict.get("metric_key") == "L-1-2": + metric_value_major_axis = axis_profile_dict.get( + "metric_value" + ) + elif axis_profile_dict.get("metric_key") == "L-3-4": + metric_value_minor_axis = axis_profile_dict.get( + "metric_value" + ) + else: + raise ValueError( + f"\nError: metric_key should be one of L-1-2 or L-3-4\n" + ) + metric_type = axis_profile_dict.get("metric_type") + metric_map_name = axis_profile_dict.get("metric_map_name") + metric_unit = axis_profile_dict.get("metric_unit") + + # Check if both major and minor axes are present. If not, skip this profile + if ( + metric_value_major_axis is None + or metric_value_minor_axis is None + ): + continue + else: + biometric_ellipses_ls.append( + { + "metric_type": metric_type, + "metric_map_name": metric_map_name, + "metric_key_major_axis": "L-1-2", + "metric_value_major_axis": metric_value_major_axis, + "metric_key_minor_axis": "L-3-4", + "metric_value_minor_axis": metric_value_minor_axis, + "metric_unit": metric_unit, + } + ) + flatten_slice_profile.append( + { + "image_file": image_file, + "landmark_file": landmark_file, + "mask_file": mask_file, + "slice_dim": slice_dim, + "slice_idx": slice_idx, + "image_size_2d": image_size_2d, + "pixel_size": pixel_size, + "image_size_3d": image_size_3d, + "voxel_size": voxel_size, + "biometric_profile": biometric_ellipses_ls, + } + ) + return flatten_slice_profile + + def process_each_task(self): + # Process each task in the benchmark plan + for task_idx, task in enumerate(self.bm_plan["tasks"], 1): + print( + f"{'='*50}\nProcessing {self.task_type} task {task_idx}/{len(self.bm_plan['tasks'])}\n{'='*50}" + ) + # Update task ID + task["task_ID"] = f"{task_idx:02d}" + # Split the dataset into training and testing sets + print("Splitting dataset into training and testing sets...") + imgs_tr, imgs_ts = self._split_niigz_dataset(task["image_folder"]) + print( + f"Split complete: {len(imgs_tr)} training, {len(imgs_ts)} testing cases\n" + ) + # Update the profile of the training and testing sets + print("Updating profiles for training set...\n") + self._update_cases_profile(imgs_tr, task, "train") + print("Updating profiles for testing set...\n") + self._update_cases_profile(imgs_ts, task, "test") + print(f"Finished processing task {task_idx}\n{'='*50}\n\n") + + def process(self): + print(f"Preprocessing {self.dataset_name} dataset in {self.dataset_dir}...\n") + self.update_tasks_number() + if self.force_uint16_mask: + self.convert_masks_to_uint16() + if self.reorient2RAS: + self.reorient_niigz_RASPlus() + self.process_each_task() + self.save_benchmark_plan() diff --git a/src/medvision_ds/utils/data_conversion.py b/src/medvision_ds/utils/data_conversion.py new file mode 100644 index 0000000000000000000000000000000000000000..76dcbda5d32ef88bbc4a6bafcd9884abe5c88eb6 --- /dev/null +++ b/src/medvision_ds/utils/data_conversion.py @@ -0,0 +1,550 @@ +import SimpleITK as sitk +import os +import glob +import nrrd +import nibabel as nib +import numpy as np +import cv2 +import traceback +import psutil +from concurrent.futures import ProcessPoolExecutor, as_completed +from pathlib import Path + + +def _reorient_niigz_RASplus(nifti_path, output_path): + """ + Load a NIfTI file, reorient it to RAS+ (right-anterior-superior) using as_closest_canonical, + and save the result while preserving the original data type. + """ + # Load the image + img = nib.load(nifti_path) + # Get original data type + original_dtype = img.get_fdata().dtype + # Check current orientation + current_orientation = nib.aff2axcodes(img.affine) + if current_orientation == ("R", "A", "S"): + msg = f"{nifti_path} is already in RAS+ orientation.\n" + if nifti_path != output_path: + nib.save(img, output_path) + return msg + # Convert to RAS+ orientation + canonical_img = nib.as_closest_canonical(img) + # Create new image with original dtype + reoriented_data = canonical_img.get_fdata().astype(original_dtype) + new_img = nib.Nifti1Image(reoriented_data, canonical_img.affine, header=img.header) + # Preserve original header information where possible + new_img.header.set_data_dtype(original_dtype) + # Save the reoriented image + nib.save(new_img, output_path) + msg = f"Converted {nifti_path} to RAS+ orientation and saved as {output_path}.\n" + return msg + + +def reorient_niigz_RASplus_batch_inplace(dataset_dir, workers_limit=1): + """ + Reorient all NIfTI files in a directory and its subdirectories to RAS+ orientation in place. + This function modifies the original files rather than creating new ones. + + Args: + dataset_dir (str): Directory containing .nii.gz files + workers_limit (int): Maximum number of worker processes. Defaults to 1. + """ + # Find all .nii.gz files recursively in directory + nii_files = list(glob.glob(f"{dataset_dir}/**/*.nii.gz", recursive=True)) + total_files = len(nii_files) + num_workers = min(workers_limit, total_files) if workers_limit > 0 else 1 + print(f"Reorienting {total_files} files to RAS+ orientation...\n") + + # Multi-process dataset reorientation + preprocessed_files_count = 0 + failed_cases = [] + with ProcessPoolExecutor(max_workers=num_workers) as executor: + futures = { + executor.submit(_reorient_niigz_RASplus, nii_file, nii_file): nii_file + for nii_file in nii_files + } + + for fut in as_completed(futures): + nii_file = futures[fut] + try: + msg = fut.result() + preprocessed_files_count += 1 + print( + f"✓ Reoriented {os.path.basename(nii_file)}: ({preprocessed_files_count}/{total_files})" + ) + print(f" - {msg}") + + mem = psutil.virtual_memory().percent + if mem > 80: + print(f"⚠️ High memory usage: {mem}%") + + except Exception: + err = traceback.format_exc() + print( + f"❌ Reorienting {os.path.basename(nii_file)} generated an exception:\n{err}" + ) + failed_cases.append((nii_file, err)) + + if failed_cases: + print(f"❌ Failed to reorient {len(failed_cases)} files:") + for nii_file, e in failed_cases: + print(f" - {os.path.basename(nii_file)}: {e.splitlines()[-1]}") + raise RuntimeError("Some tasks failed to reorient. See logs above.") + + +def convert_nrrd_to_nifti(input_dir, output_dir, recursive=False): + """ + Convert all .nrrd files in input_dir to .nii.gz files in output_dir + + Args: + input_dir (str): Directory containing .nrrd files + output_dir (str): Directory to save .nii.gz files + recursive (bool): If True, search for .nrrd files in subdirectories + """ + # Create output directory if it doesn't exist + Path(output_dir).mkdir(parents=True, exist_ok=True) + + # Get all .nrrd files in input directory + pattern = "**/*.nrrd" if recursive else "*.nrrd" + nrrd_files = list(Path(input_dir).glob(pattern)) + + print(f"Found {len(nrrd_files)} .nrrd files") + + for nrrd_file in nrrd_files: + try: + print(f"Converting {nrrd_file.name}") + + # Read NRRD file + data, header = nrrd.read(str(nrrd_file)) + + # Get spacing (voxel size) + space_directions = header.get("space directions") + if space_directions is not None: + voxel_size = np.array( + [np.linalg.norm(dir) for dir in space_directions if dir is not None] + ) + print("Voxel dimensions calculated from spatial direction matrix") + else: + raise ValueError( + "No space directions found in NRRD header. Cannot determine voxel size." + ) + + # Get origin + origin = header.get("space origin", [0.0, 0.0, 0.0]) + + # Create affine matrix + affine = np.eye(4) + if space_directions is not None: + affine[:3, :3] = np.array( + [dir if dir is not None else [0, 0, 0] for dir in space_directions] + ) + else: + affine[:3, :3] = np.diag(voxel_size) + affine[:3, 3] = origin + + # Create NIfTI image + nifti_img = nib.Nifti1Image(data, affine) + + # Set additional header information + nifti_header = nifti_img.header + nifti_header.set_zooms(voxel_size) + + # Create output filename + output_file = Path(output_dir) / f"{nrrd_file.stem}.nii.gz" + + # Save NIfTI file + nib.save(nifti_img, str(output_file)) + print(f"Saved to {output_file}") + + except Exception as e: + print(f"Error converting {nrrd_file.name}: {e}") + + +def convert_mha_to_nifti(input_dir, output_dir, recursive=False): + """ + Convert all .mha files in input_dir to .nii.gz files in output_dir + + Args: + input_dir (str): Directory containing .mha files + output_dir (str): Directory to save .nii.gz files + recursive (bool): If True, search for .mha files in subdirectories + """ + # Create output directory if it doesn't exist + Path(output_dir).mkdir(parents=True, exist_ok=True) + + # Get all .mha files in input directory + pattern = "**/*.mha" if recursive else "*.mha" + mha_files = list(Path(input_dir).glob(pattern)) + + print(f"Found {len(mha_files)} .mha files") + + for mha_file in mha_files: + try: + # Read .mha file + print(f"Converting {mha_file.name}") + image = sitk.ReadImage(str(mha_file)) + + # Create output filename + output_file = Path(output_dir) / f"{mha_file.stem}.nii.gz" + + # Write as .nii.gz + sitk.WriteImage(image, str(output_file)) + print(f"Saved to {output_file}") + + except Exception as e: + print(f"Error converting {mha_file.name}: {e}") + + +def convert_nii_to_niigz(input_dir, output_dir, recursive=False): + """ + Convert all .nii files in input_dir to .nii.gz files in output_dir + + Args: + input_dir (str): Directory containing .nii files + output_dir (str): Directory to save .nii.gz files + recursive (bool): If True, search for .nii files in subdirectories + """ + # Create output directory if it doesn't exist + Path(output_dir).mkdir(parents=True, exist_ok=True) + + # Get all .nii files in input directory + pattern = "**/*.nii" if recursive else "*.nii" + nii_files = list(Path(input_dir).glob(pattern)) + + print(f"Found {len(nii_files)} .nii files") + + for nii_file in nii_files: + try: + # Read .nii file + print(f"Converting {nii_file.name}") + image = sitk.ReadImage(str(nii_file)) + + # Create output filename + output_file = Path(output_dir) / f"{nii_file.stem}.nii.gz" + + # Write as .nii.gz + sitk.WriteImage(image, str(output_file)) + print(f"Saved to {output_file}") + + except Exception as e: + print(f"Error converting {nii_file.name}: {e}") + + +def _convert_mask_to_uint16(mask_path): + # Load nii + nii = nib.load(mask_path) + hdr = nii.header.copy() + + # Convert data to uint16 type + # NOTE: When you cast to uint16 in NumPy, it truncates toward zero, it does not round + # e.g., 1.99995422.astype(np.uint16) → 1 + data = np.rint(nii.get_fdata()).astype(np.uint16) + + # Force header consistency + if hdr.get_data_dtype() != np.dtype("uint16"): + hdr.set_data_dtype(np.uint16) + + # Force no scaling + slope, inter = hdr.get_slope_inter() + # NOTE: In NIfTI headers, scl_slope and scl_inter can be stored as NaN to mean "no scaling", i.e., both (1, 0) or (NaN, NaN) mean "no scaling" + # Check if slope and inter are numeric before using np.isfinite + slope_valid = slope is not None and np.isfinite(slope) and slope == 1 + inter_valid = inter is not None and np.isfinite(inter) and inter == 0 + if not (slope_valid and inter_valid): + hdr.set_slope_inter(1.0, 0.0) + + out = nib.Nifti1Image(data, nii.affine, hdr) + nib.save(out, mask_path) + + +def convert_mask_to_uint16_per_dir(mask_folder, workers_limit=1): + """ + Convert all .nii.gz mask files in a folder to uint16 data type with proper header settings. + This is useful for segmentation masks where we want integer labels without scaling. + + Args: + mask_folder (str): Path to folder containing mask files + """ + # List all .nii.gz files in the mask folder + mask_files = [f for f in os.listdir(mask_folder) if f.endswith(".nii.gz")] + total_files = len(mask_files) + num_workers = min(workers_limit, total_files) if workers_limit > 0 else 1 + print(f"Found {total_files} .nii.gz mask files to convert") + + # Multi-process dataset concatenation + preprocessed_files_count = 0 + failed_cases = [] + with ProcessPoolExecutor(max_workers=num_workers) as executor: + futures = { + executor.submit( + _convert_mask_to_uint16, os.path.join(mask_folder, mask_file) + ): mask_file + for mask_file in mask_files + } + + for fut in as_completed(futures): + mask_file = futures[fut] + try: + fut.result() + preprocessed_files_count += 1 + print( + f"✓ Converted {mask_file}: ({preprocessed_files_count}/{total_files})" + ) + + mem = psutil.virtual_memory().percent + if mem > 80: + print(f"⚠️ High memory usage: {mem}%") + + except Exception: + err = traceback.format_exc() + print(f"❌ Converting {mask_file} generated an exception:\n{err}") + failed_cases.append((mask_file, err)) + if failed_cases: + print(f"❌ Failed to preprocessed {len(failed_cases)} files:") + for mask_file, e in failed_cases: + print(f" - {mask_file}: {e.splitlines()[-1]}") + raise RuntimeError("Some tasks failed to load. See logs above.") + + +def _copy_img_header_to_mask(img_file, mask_dir): + base_name = os.path.basename(img_file) + mask_file = os.path.join(mask_dir, base_name) + if os.path.exists(mask_file): + img = nib.load(img_file) + mask = nib.load(mask_file) + new_mask = nib.Nifti1Image(mask.get_fdata(), img.affine) + nib.save(new_mask, mask_file) + return mask_file + + +def copy_img_header_to_mask(img_files, mask_dir, workers_limit=1): + assert os.path.exists(mask_dir), "mask_dir must exist" + total_files = len(img_files) + num_workers = min(workers_limit, total_files) if workers_limit > 0 else 1 + print(f"Found {total_files} .nii.gz mask files to convert") + + # Multi-process dataset concatenation + preprocessed_files_count = 0 + failed_cases = [] + with ProcessPoolExecutor(max_workers=num_workers) as executor: + futures = { + executor.submit(_copy_img_header_to_mask, img_file, mask_dir): img_file + for img_file in img_files + } + + for fut in as_completed(futures): + img_file = futures[fut] + try: + mask_file = fut.result() + preprocessed_files_count += 1 + print( + f"✓ Converted {mask_file}: ({preprocessed_files_count}/{total_files})" + ) + + mem = psutil.virtual_memory().percent + if mem > 80: + print(f"⚠️ High memory usage: {mem}%") + + except Exception: + err = traceback.format_exc() + print( + f"❌ Copying header from {img_file} generated an exception:\n{err}" + ) + failed_cases.append((img_file, err)) + if failed_cases: + print(f"❌ Failed to preprocessed {len(failed_cases)} files:") + for img_file, e in failed_cases: + print(f" - {img_file}: {e.splitlines()[-1]}") + raise RuntimeError("Some tasks failed to load. See logs above.") + + +def convert_bmp_to_niigz( + bmp_dir, + niigz_dir, + slice_dim_type, + pseudo_voxel_size, + flip_dim0=False, + flip_dim1=False, + swap_dim01=False, +): + """ + Convert BMP image files to NIfTI (.nii.gz) format. + This function converts 2D BMP images to 3D NIfTI volumes with specified slice orientation. + The output NIfTI files will have RAS+ orientation with specified voxel size. + Args: + bmp_dir (str): Input directory containing BMP files to convert + niigz_dir (str): Output directory where NIfTI files will be saved + slice_dim_type (int): Slice dimension/orientation type: + 0: Sagittal (YZ plane) + 1: Coronal (XZ plane) + 2: Axial (XY plane) + pseudo_voxel_size (list): List of 3 floats specifying voxel dimensions in mm [x,y,z] + flip_dim0 (bool, optional): If True, flip image along dimension 0. Defaults to False. + flip_dim1 (bool, optional): If True, flip image along dimension 1. Defaults to False. + swap_dim01 (bool, optional): If True, swap dimensions 0 and 1. Defaults to False. + Returns: + tuple: Original image dimensions (height, width) of the first converted BMP + """ + + # Validate slice_dim_type + if slice_dim_type not in [0, 1, 2]: + raise ValueError("slice_dim_type must be 0, 1, or 2") + + # Convert pseudo_voxel_size to list if it's not already + pseudo_voxel_size = list(pseudo_voxel_size) + + # Create output directory + Path(niigz_dir).mkdir(parents=True, exist_ok=True) + + # Get all BMP files + bmp_files = list(Path(bmp_dir).glob("*.bmp")) + print(f"Found {len(bmp_files)} .bmp files") + + for bmp_file in bmp_files: + try: + print(f"Converting {bmp_file.name}") + + # Read BMP image + img_2d = cv2.imread(str(bmp_file), cv2.IMREAD_GRAYSCALE) + img_size_dim0, img_size_dim1 = img_2d.shape + + # Note: this is definitely correct, DO NOT SWAP the order of transformations + if flip_dim0: + img_2d = cv2.flip(img_2d, 0) # 0 means flip vertically + if flip_dim1: + img_2d = cv2.flip(img_2d, 1) # 1 means flip horizontally + if swap_dim01: # this line should be AFTER slip_x and slip_y + img_2d = np.swapaxes(img_2d, 0, 1) + + # Create 3D array based on slice_dim_type + if slice_dim_type == 0: # Sagittal (YZ plane) + img_3d = np.zeros( + (1, img_2d.shape[0], img_2d.shape[1]), dtype=img_2d.dtype + ) + img_3d[0, :, :] = img_2d + elif slice_dim_type == 1: # Coronal (XZ plane) + img_3d = np.zeros( + (img_2d.shape[0], 1, img_2d.shape[1]), dtype=img_2d.dtype + ) + img_3d[:, 0, :] = img_2d + else: # Axial (XY plane) + img_3d = np.zeros( + (img_2d.shape[0], img_2d.shape[1], 1), dtype=img_2d.dtype + ) + img_3d[:, :, 0] = img_2d + + # Create affine matrix for RAS+ orientation + # Set voxel size to 0.1mm in all dimensions + affine = np.diag(pseudo_voxel_size + [1]) + + # Create NIfTI image + nii_img = nib.Nifti1Image(img_3d, affine) + + # Set header information + nii_img.header.set_zooms(pseudo_voxel_size) + + # Save as NIfTI file + output_file = Path(niigz_dir) / f"{bmp_file.stem}.nii.gz" + nib.save(nii_img, str(output_file)) + print(f"Saved to {output_file}") + + except Exception as e: + print(f"Error converting {bmp_file.name}: {e}") + + return img_size_dim0, img_size_dim1 + + +def convert_jpg_to_niigz( + jpg_dir, + niigz_dir, + slice_dim_type, + pseudo_voxel_size, + flip_dim0=False, + flip_dim1=False, + swap_dim01=False, +): + """ + Convert JPG image files to NIfTI (.nii.gz) format. + This function converts 2D JPG images to 3D NIfTI volumes with specified slice orientation. + The output NIfTI files will have RAS+ orientation with specified voxel size. + Args: + jpg_dir (str): Input directory containing JPG files to convert + niigz_dir (str): Output directory where NIfTI files will be saved + slice_dim_type (int): Slice dimension/orientation type: + 0: Sagittal (YZ plane) + 1: Coronal (XZ plane) + 2: Axial (XY plane) + pseudo_voxel_size (list): List of 3 floats specifying voxel dimensions in mm [x,y,z] + flip_dim0 (bool, optional): If True, flip image along dimension 0. Defaults to False. + flip_dim1 (bool, optional): If True, flip image along dimension 1. Defaults to False. + swap_dim01 (bool, optional): If True, swap dimensions 0 and 1. Defaults to False. + Returns: + tuple: Original image dimensions (height, width) of the first converted JPG + """ + + # Validate slice_dim_type + if slice_dim_type not in [0, 1, 2]: + raise ValueError("slice_dim_type must be 0, 1, or 2") + + # Convert pseudo_voxel_size to list if it's not already + pseudo_voxel_size = list(pseudo_voxel_size) + + # Create output directory + Path(niigz_dir).mkdir(parents=True, exist_ok=True) + + # Get all JPG files + jpg_files = list(Path(jpg_dir).glob("*.jpg")) + print(f"Found {len(jpg_files)} .jpg files") + + for jpg_file in jpg_files: + try: + print(f"Converting {jpg_file.name}") + + # Read JPG image + img_2d = cv2.imread(str(jpg_file), cv2.IMREAD_GRAYSCALE) + img_size_dim0, img_size_dim1 = img_2d.shape + + # Note: this is definitely correct, DO NOT SWAP the order of transformations + if flip_dim0: + img_2d = cv2.flip(img_2d, 0) # 0 means flip vertically + if flip_dim1: + img_2d = cv2.flip(img_2d, 1) # 1 means flip horizontally + if swap_dim01: # this line should be AFTER flip_dim0 and flip_dim1 + img_2d = np.swapaxes(img_2d, 0, 1) + + # Create 3D array based on slice_dim_type + if slice_dim_type == 0: # Sagittal (YZ plane) + img_3d = np.zeros( + (1, img_2d.shape[0], img_2d.shape[1]), dtype=img_2d.dtype + ) + img_3d[0, :, :] = img_2d + elif slice_dim_type == 1: # Coronal (XZ plane) + img_3d = np.zeros( + (img_2d.shape[0], 1, img_2d.shape[1]), dtype=img_2d.dtype + ) + img_3d[:, 0, :] = img_2d + else: # Axial (XY plane) + img_3d = np.zeros( + (img_2d.shape[0], img_2d.shape[1], 1), dtype=img_2d.dtype + ) + img_3d[:, :, 0] = img_2d + + # Create affine matrix for RAS+ orientation + # Set voxel size to 0.1mm in all dimensions + affine = np.diag(pseudo_voxel_size + [1]) + + # Create NIfTI image + nii_img = nib.Nifti1Image(img_3d, affine) + + # Set header information + nii_img.header.set_zooms(pseudo_voxel_size) + + # Save as NIfTI file + output_file = Path(niigz_dir) / f"{jpg_file.stem}.nii.gz" + nib.save(nii_img, str(output_file)) + print(f"Saved to {output_file}") + + except Exception as e: + print(f"Error converting {jpg_file.name}: {e}") + + return img_size_dim0, img_size_dim1 diff --git a/src/medvision_ds/utils/doc_to_visual_utils.py b/src/medvision_ds/utils/doc_to_visual_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9dfc832d3cf327528ab6a9f8625160a86a7252c6 --- /dev/null +++ b/src/medvision_ds/utils/doc_to_visual_utils.py @@ -0,0 +1,557 @@ +from PIL import ImageDraw, ImageFont, Image +import numpy as np +import cv2 + + +def _get_text_dimensions(draw, text, font): + """ + Calculate the width and height of text in pixels. + + Args: + draw: ImageDraw object + text: String of text to measure + font: ImageFont object + + Returns: + tuple: (width, height) in pixels + """ + # Get the bounding box of the text + bbox = draw.textbbox((0, 0), text, font=font) + + # Calculate width and height from the bounding box + # bbox returns (left, top, right, bottom) + width = bbox[2] - bbox[0] + height = bbox[3] - bbox[1] + + return width, height + + +def add_landmarks_and_line_overlay(pil_img, p1_coords, p2_coords): + """ + Add landmarks (points) and a line connecting them to an image. + + Args: + pil_img: PIL Image in RGB or RGBA format + p1_coords: List of [dim0, dim1] coordinates for the first point + p2_coords: List of [dim0, dim1] coordinates for the second point + + Returns: + PIL Image with the landmarks and connecting line overlay + """ + # Create a drawing object + draw = ImageDraw.Draw(pil_img) + + # Convert coordinates format (similar to bbox function) + # First coordinate is height (y) and second is width (x) + x1, y1 = p1_coords[1], p1_coords[0] + x2, y2 = p2_coords[1], p2_coords[0] + + # Draw the line connecting the points (green) + draw.line([(x1, y1), (x2, y2)], fill="#00FF00", width=2) + + # Draw the points (red) + point_radius = 3 + draw.ellipse( + [ + (x1 - point_radius, y1 - point_radius), + (x1 + point_radius, y1 + point_radius), + ], + fill="#FF0000", + ) + draw.ellipse( + [ + (x2 - point_radius, y2 - point_radius), + (x2 + point_radius, y2 + point_radius), + ], + fill="#FF0000", + ) + + return pil_img + + +def add_bbox_overlay(pil_img, bbox_min_coords, bbox_max_coords): + """ + Add a bounding box overlay to an image. + + Args: + pil_img: PIL Image in RGB or RGBA format + bbox_min_coords: List of [dim0_min, dim1_min] coordinates for the top-left corner of the bounding box + bbox_max_coords: List of [dim0_max, dim1_max] coordinates for the bottom-right corner of the bounding box + + NOTE: For the coordinate definition in the MedVision dataset, please refer to the + `medvision_ds.utils.benchmark_planner.MedVision_BenchmarkPlannerDetection._find_bounding_boxes_2D` + + Returns: + PIL Image with the bounding box overlay + """ + # NOTE: For bbox_min_coords and bbox_max_coords: + # the first coordinate is the height (y-axis) direction and the second is the width (x-axis) direction; + # the origin is at the upper-left corner of the image. + # For PIL Image, the origin is at the upper-left corner of the image. + # So, x-coordinate = dim1_coordinate, y-coordinate = dim0_coordinate + + # Convert input bounding box corrdinates to xy coordinates for PIL Image + x_min = bbox_min_coords[1] + y_min = bbox_min_coords[0] + x_max = bbox_max_coords[1] + y_max = bbox_max_coords[0] + + # ref: https://pillow.readthedocs.io/en/stable/reference/ImageDraw.html + draw = ImageDraw.Draw(pil_img) + draw.rectangle([(x_min, y_min), (x_max, y_max)], outline="#00FF00", width=2) + return pil_img + + +def add_bbox_overlay_solid(pil_img, bbox_min_coords, bbox_max_coords): + """ + Add a semi-transparent solid bounding box overlay to an image. + + Args: + pil_img: PIL Image in RGB or RGBA format + bbox_min_coords: List of [dim0_min, dim1_min] coordinates for the top-left corner of the bounding box + bbox_max_coords: List of [dim0_max, dim1_max] coordinates for the bottom-right corner of the bounding box + + NOTE: For the coordinate definition in the MedVision dataset, please refer to the + `medvision_ds.utils.benchmark_planner.MedVision_BenchmarkPlannerDetection._find_bounding_boxes_2D` + + Returns: + PIL Image with the solid bounding box overlay + """ + # NOTE: For bbox_min_coords and bbox_max_coords: + # the first coordinate is the height (y-axis) direction and the second is the width (x-axis) direction; + # the origin is at the upper-left corner of the image. + # For PIL Image, the origin is at the upper-left corner of the image. + # So, x-coordinate = dim1_coordinate, y-coordinate = dim0_coordinate + + # Convert input bounding box corrdinates to xy coordinates for PIL Image + x_min = bbox_min_coords[1] + y_min = bbox_min_coords[0] + x_max = bbox_max_coords[1] + y_max = bbox_max_coords[0] + + # Convert to RGBA mode to support transparency + pil_img = pil_img.convert("RGBA") + + # ref: https://pillow.readthedocs.io/en/stable/reference/ImageDraw.html + draw = ImageDraw.Draw(pil_img) + + # Draw a filled rectangle with semi-transparent green (25% opacity) + draw.rectangle([(x_min, y_min), (x_max, y_max)], fill=(0, 255, 0, 64)) + + return pil_img + + +def add_mask_overlay_contour(pil_img, mask_2d_binary): + """ + Add a green contour outline to an image based on a binary mask. + + Args: + pil_img: PIL Image in RGB or RGBA format + mask_2d_binary: Binary numpy array representing the mask + + Returns: + PIL Image with the mask contour overlaid in green + """ + + # Convert PIL image to numpy array for OpenCV + img_np = np.array(pil_img) + + # Make sure mask is the right type and size + mask = mask_2d_binary.astype(np.uint8) + if mask.shape[:2] != img_np.shape[:2]: + mask = cv2.resize( + mask, (img_np.shape[1], img_np.shape[0]), interpolation=cv2.INTER_NEAREST + ) + + # Find contours in the mask + contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + # Create a copy of the image to draw on + img_with_contour = img_np.copy() + + # Draw contours on the image + cv2.drawContours(img_with_contour, contours, -1, (0, 255, 0), 2) + + # Convert back to PIL + return Image.fromarray(img_with_contour) + + +def add_mask_overlay_solid(pil_img, mask_2d_binary): + """ + Add a semi-transparent green overlay to an image based on a binary mask. + + Args: + pil_img: PIL Image in RGB or RGBA format + mask_2d_binary: Binary numpy array representing the mask + + Returns: + PIL Image with the mask overlaid in green + """ + # Create a green overlay image + overlay = Image.new("RGBA", pil_img.size, (0, 255, 0, 0)) + + # Convert mask to PIL image format and resize if needed + mask_pil = Image.fromarray((mask_2d_binary * 64).astype(np.uint8), mode="L") + if mask_pil.size != pil_img.size: + mask_pil = mask_pil.resize(pil_img.size) + + # Set the mask as the alpha channel for the overlay + overlay.putalpha(mask_pil) + + # Convert original image to RGBA + pil_img = pil_img.convert("RGBA") + + # Composite the images + pil_img = Image.alpha_composite(pil_img, overlay) + + # Convert back to RGB for display + pil_img = pil_img.convert("RGB") + + return pil_img + + +def add_scale_label(pil_img, pixel_sizes, slice_dim): + """Add scale label to image.""" + draw = ImageDraw.Draw(pil_img) + + # Get image dimensions - in PIL, size returns (width, height) + img_width, img_height = pil_img.size + + # Define a class with the _get_appropriate_scale method + class ScaleCalculator: + def _get_appropriate_scale(self, pixel_size, img_size, init_scale=10): + scales = [1, 2, 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100] + scale_pixels_num = int(init_scale / pixel_size) + min_pixels = img_size * 0.05 + max_pixels = img_size * 0.25 + + if scale_pixels_num < min_pixels: + for scale in scales: + if scale > init_scale: + return self._get_appropriate_scale(pixel_size, img_size, scale) + elif scale_pixels_num > max_pixels: + for scale in reversed(scales): + if scale < init_scale: + return self._get_appropriate_scale(pixel_size, img_size, scale) + + return init_scale, scale_pixels_num + + scale_calculator = ScaleCalculator() + + # Find which dimension is smaller + # In the 2D array: height = first dimension, width = second dimension + # In pixel_sizes: [height_scale, width_scale] + # In PIL image: img_width = second dimension, img_height = first dimension + if img_height < img_width: # Height is the smaller dimension + pixel_size_min = pixel_sizes[0] # Height pixel size + image_dim_min = img_height + else: # Width is the smaller dimension + pixel_size_min = pixel_sizes[1] # Width pixel size + image_dim_min = img_width + + # Calculate appropriate scale + scale_mm, scale_pixels_min = scale_calculator._get_appropriate_scale( + pixel_size_min, image_dim_min, init_scale=10 + ) + + # Calculate scale for the other dimension + if img_height < img_width: + scale_pixels_height = scale_pixels_min + scale_pixels_width = int(scale_mm / pixel_sizes[1]) + else: + scale_pixels_width = scale_pixels_min + scale_pixels_height = int(scale_mm / pixel_sizes[0]) + + # Position for scale bar (5% from the edge) + start_x, start_y = int(img_width * 0.05), int(img_height * 0.05) + end_x, end_y = start_x + scale_pixels_width, start_y + scale_pixels_height + + # Set text font and scale line width + default_line_width = 2 + default_fontsize = 10 + line_width = default_line_width + font = ImageFont.load_default().font_variant(size=default_fontsize) + + # Draw horizontal line + draw.line( + [(start_x, start_y), (end_x, start_y)], + fill=(255, 255, 255), + width=line_width, + ) + # Draw vertical line + draw.line( + [(start_x, start_y), (start_x, end_y)], + fill=(255, 255, 255), + width=line_width, + ) + # Add scale text + draw.text( + (start_x + 5, start_y + 5), f"{scale_mm} mm", fill=(255, 255, 255), font=font + ) + + return pil_img + + +def add_scale_label_v2(pil_img, pixel_sizes, slice_dim): + """Add scale label to image.""" + draw = ImageDraw.Draw(pil_img) + + # Get image dimensions - in PIL, size returns (width, height) + img_width, img_height = pil_img.size + + # Define a class with the _get_appropriate_scale method + class ScaleCalculator: + def _get_appropriate_scale(self, pixel_size, img_size, init_scale=10): + scales = [1, 2, 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100] + scale_pixels_num = int(init_scale / pixel_size) + min_pixels = img_size * 0.05 + max_pixels = img_size * 0.25 + + if scale_pixels_num < min_pixels: + for scale in scales: + if scale > init_scale: + return self._get_appropriate_scale(pixel_size, img_size, scale) + elif scale_pixels_num > max_pixels: + for scale in reversed(scales): + if scale < init_scale: + return self._get_appropriate_scale(pixel_size, img_size, scale) + + return init_scale, scale_pixels_num + + scale_calculator = ScaleCalculator() + + # Find which dimension is smaller + # In the 2D array: height = first dimension, width = second dimension + # In pixel_sizes: [height_scale, width_scale] + # In PIL image: img_width = second dimension, img_height = first dimension + if img_height < img_width: # Height is the smaller dimension + pixel_size_min = pixel_sizes[0] # Height pixel size + image_dim_min = img_height + else: # Width is the smaller dimension + pixel_size_min = pixel_sizes[1] # Width pixel size + image_dim_min = img_width + + # Calculate appropriate scale + scale_mm, scale_pixels_min = scale_calculator._get_appropriate_scale( + pixel_size_min, image_dim_min, init_scale=10 + ) + + # Calculate scale for the other dimension + if img_height < img_width: + scale_pixels_height = scale_pixels_min + scale_pixels_width = int(scale_mm / pixel_sizes[1]) + else: + scale_pixels_width = scale_pixels_min + scale_pixels_height = int(scale_mm / pixel_sizes[0]) + + # Position for scale bar (5% from the edge) + start_x, start_y = int(img_width * 0.05), int(img_height * 0.05) + end_x, end_y = start_x + scale_pixels_width, start_y + scale_pixels_height + + # Set text font and scale line width + default_line_width = 2 + default_fontsize = 10 + line_width = default_line_width + font = ImageFont.load_default().font_variant(size=default_fontsize) + + # Draw horizontal line + draw.line( + [(start_x, start_y), (end_x, start_y)], + fill=(255, 255, 255), + width=line_width, + ) + # Draw vertical line + draw.line( + [(start_x, start_y), (start_x, end_y)], + fill=(255, 255, 255), + width=line_width, + ) + # Add scale text + draw.text( + (start_x + 5, start_y + 5), f"{scale_mm} mm", fill=(255, 255, 255), font=font + ) + + return pil_img, scale_mm + + +def add_scale_label_autoGreen(pil_img, pixel_sizes, slice_dim): + """Add scale label to image.""" + draw = ImageDraw.Draw(pil_img) + + # Get image dimensions - in PIL, size returns (width, height) + img_width, img_height = pil_img.size + + # Define a class with the _get_appropriate_scale method + class ScaleCalculator: + def _get_appropriate_scale(self, pixel_size, img_size, init_scale=10): + scales = [1, 2, 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100] + scale_pixels_num = int(init_scale / pixel_size) + min_pixels = img_size * 0.05 + max_pixels = img_size * 0.25 + + if scale_pixels_num < min_pixels: + for scale in scales: + if scale > init_scale: + return self._get_appropriate_scale(pixel_size, img_size, scale) + elif scale_pixels_num > max_pixels: + for scale in reversed(scales): + if scale < init_scale: + return self._get_appropriate_scale(pixel_size, img_size, scale) + + return init_scale, scale_pixels_num + + scale_calculator = ScaleCalculator() + + # Find which dimension is smaller + # In the 2D array: height = first dimension, width = second dimension + # In pixel_sizes: [height_scale, width_scale] + # In PIL image: img_width = second dimension, img_height = first dimension + if img_height < img_width: # Height is the smaller dimension + pixel_size_min = pixel_sizes[0] # Height pixel size + image_dim_min = img_height + else: # Width is the smaller dimension + pixel_size_min = pixel_sizes[1] # Width pixel size + image_dim_min = img_width + + # Calculate appropriate scale + scale_mm, scale_pixels_min = scale_calculator._get_appropriate_scale( + pixel_size_min, image_dim_min, init_scale=10 + ) + + # Calculate scale for the other dimension + if img_height < img_width: + scale_pixels_height = scale_pixels_min + scale_pixels_width = int(scale_mm / pixel_sizes[1]) + else: + scale_pixels_width = scale_pixels_min + scale_pixels_height = int(scale_mm / pixel_sizes[0]) + + # Position for scale bar (5% from the edge) + start_x, start_y = int(img_width * 0.05), int(img_height * 0.05) + end_x, end_y = start_x + scale_pixels_width, start_y + scale_pixels_height + + # Set text font and scale line width + default_line_width = 2 + default_fontsize = 10 + line_width = default_line_width + font = ImageFont.load_default().font_variant(size=default_fontsize) + text = f"{scale_mm} mm" + _, tmp_text_height = _get_text_dimensions(draw, text, font) + if tmp_text_height < img_height * 0.05: + fontsize = int(default_fontsize * (img_height * 0.05) / tmp_text_height) + font = ImageFont.load_default().font_variant(size=fontsize) + if (img_height * 0.05) / tmp_text_height > 5: + line_width = int( + default_line_width * (img_height * 0.05) / tmp_text_height / 5 + ) + + # Add scale text + draw.text((start_x + 5, start_y + 5), text, fill=(0, 255, 0), font=font) + # Draw horizontal line + draw.line( + [(start_x, start_y), (end_x, start_y)], + fill=(0, 255, 0), + width=line_width, + ) + # Draw vertical line + draw.line( + [(start_x, start_y), (start_x, end_y)], + fill=(0, 255, 0), + width=line_width, + ) + + return pil_img + + +def add_scale_and_orientation_label(pil_img, pixel_sizes, slice_dim): + """Add scale bar and orientation labels to image.""" + draw = ImageDraw.Draw(pil_img) + + # Get image dimensions - in PIL, size returns (width, height) + img_width, img_height = pil_img.size + + # Define a class with the _get_appropriate_scale method + class ScaleCalculator: + def _get_appropriate_scale(self, pixel_size, img_size, init_scale=10): + scales = [1, 2, 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100] + scale_pixels_num = int(init_scale / pixel_size) + min_pixels = img_size * 0.05 + max_pixels = img_size * 0.25 + + if scale_pixels_num < min_pixels: + for scale in scales: + if scale > init_scale: + return self._get_appropriate_scale(pixel_size, img_size, scale) + elif scale_pixels_num > max_pixels: + for scale in reversed(scales): + if scale < init_scale: + return self._get_appropriate_scale(pixel_size, img_size, scale) + + return init_scale, scale_pixels_num + + scale_calculator = ScaleCalculator() + + # Find which dimension is smaller + # In the 2D array: height = first dimension, width = second dimension + # In pixel_sizes: [height_scale, width_scale] + # In PIL image: img_width = second dimension, img_height = first dimension + if img_height < img_width: # Height is the smaller dimension + pixel_size_min = pixel_sizes[0] # Height pixel size + image_dim_min = img_height + else: # Width is the smaller dimension + pixel_size_min = pixel_sizes[1] # Width pixel size + image_dim_min = img_width + + # Calculate appropriate scale + scale_mm, scale_pixels_min = scale_calculator._get_appropriate_scale( + pixel_size_min, image_dim_min, init_scale=10 + ) + + # Calculate scale for the other dimension + if img_height < img_width: + scale_pixels_height = scale_pixels_min + scale_pixels_width = int(scale_mm / pixel_sizes[1]) + else: + scale_pixels_width = scale_pixels_min + scale_pixels_height = int(scale_mm / pixel_sizes[0]) + + # Position for scale bar (5% from the edge) + start_x, start_y = int(img_width * 0.05), int(img_height * 0.05) + end_x, end_y = start_x + scale_pixels_width, start_y + scale_pixels_height + + # Set text font and scale line width + default_line_width = 2 + default_fontsize = 10 + line_width = default_line_width + font = ImageFont.load_default().font_variant(size=default_fontsize) + + # Draw horizontal line + draw.line( + [(start_x, start_y), (end_x, start_y)], + fill=(255, 255, 255), + width=line_width, + ) + # Draw vertical line + draw.line( + [(start_x, start_y), (start_x, end_y)], + fill=(255, 255, 255), + width=line_width, + ) + # Add scale text + draw.text( + (start_x + 5, start_y + 5), f"{scale_mm} mm", fill=(255, 255, 255), font=font + ) + + # Add orientation labels based on slice_dim + label_padding = 10 + if slice_dim == 0: + draw.text((start_x, end_y + 5), "Anterior", fill=(255, 255, 255), font=font) + draw.text((end_x + 5, start_y), "Superior", fill=(255, 255, 255), font=font) + elif slice_dim == 1: + draw.text((start_x, end_y + 5), "Right", fill=(255, 255, 255), font=font) + draw.text((end_x + 5, start_y), "Superior", fill=(255, 255, 255), font=font) + else: + draw.text((start_x, end_y + 5), "Right", fill=(255, 255, 255), font=font) + draw.text((end_x + 5, start_y), "Anterior", fill=(255, 255, 255), font=font) + + return pil_img diff --git a/src/medvision_ds/utils/large_file_handler.py b/src/medvision_ds/utils/large_file_handler.py new file mode 100644 index 0000000000000000000000000000000000000000..72b727a319b0a0deba92e67ec3776a7a24c03278 --- /dev/null +++ b/src/medvision_ds/utils/large_file_handler.py @@ -0,0 +1,143 @@ +import math +import argparse +from pathlib import Path + + +# ========================= +# Usage: +# Split a large file into chunks: +# python large_file_handler.py split /path/to/large/file.nii.gz --size 40000 +# Join chunks back into the original file: +# python large_file_handler.py join /path/to/file.nii.gz.chunks +# ========================= + + +def split_file(file_path, chunk_size_mb=1000): + """ + Split a large file into smaller chunks + + Args: + file_path: Path to the file to split + chunk_size_mb: Size of each chunk in megabytes (default 1000MB = ~1GB) + """ + file_path = Path(file_path) + if not file_path.exists(): + print(f"Error: File {file_path} not found!") + return + + # Convert MB to bytes + chunk_size = chunk_size_mb * 1024 * 1024 + file_size = file_path.stat().st_size + + # Calculate number of chunks needed + num_chunks = math.ceil(file_size / chunk_size) + + print( + f"Splitting {file_path} ({file_size/1024/1024/1024:.2f} GB) into {num_chunks} chunks of {chunk_size_mb} MB each" + ) + + # Create a directory for the chunks + chunk_dir = file_path.with_suffix(".chunks") + chunk_dir.mkdir(exist_ok=True) + + # Create a manifest file + with open(chunk_dir / "manifest.txt", "w") as manifest: + manifest.write(f"original_file: {file_path.name}\n") + manifest.write(f"total_size: {file_size}\n") + manifest.write(f"chunk_size: {chunk_size}\n") + manifest.write(f"num_chunks: {num_chunks}\n") + + # Split the file + with open(file_path, "rb") as f: + for i in range(num_chunks): + chunk_file = chunk_dir / f"{file_path.name}.part{i:04d}" + print(f" Creating chunk {i+1}/{num_chunks}: {chunk_file.name}") + + with open(chunk_file, "wb") as chunk: + chunk_data = f.read(chunk_size) + chunk.write(chunk_data) + + print(f"Split complete! Chunks stored in {chunk_dir}") + return chunk_dir + + +def join_file(chunks_dir): + """ + Join file chunks back into the original file + + Args: + chunks_dir: Directory containing the chunks and manifest + """ + chunks_dir = Path(chunks_dir) + if not chunks_dir.exists() or not chunks_dir.is_dir(): + print(f"Error: Chunks directory {chunks_dir} not found!") + return + + manifest_path = chunks_dir / "manifest.txt" + if not manifest_path.exists(): + print(f"Error: Manifest file not found in {chunks_dir}") + return + + # Parse manifest + manifest = {} + with open(manifest_path, "r") as f: + for line in f: + key, value = line.strip().split(": ", 1) + manifest[key] = value + + original_filename = manifest["original_file"] + num_chunks = int(manifest["num_chunks"]) + + # Path for the restored file (in parent directory of chunks) + output_file = chunks_dir.parent / original_filename + + print(f"Joining {num_chunks} chunks into {output_file}") + + with open(output_file, "wb") as outfile: + for i in range(num_chunks): + chunk_file = chunks_dir / f"{original_filename}.part{i:04d}" + print(f" Adding chunk {i+1}/{num_chunks}: {chunk_file.name}") + + if not chunk_file.exists(): + print(f"Error: Chunk file {chunk_file} not found!") + return + + with open(chunk_file, "rb") as chunk: + outfile.write(chunk.read()) + + print(f"Join complete! Restored file: {output_file}") + + +def main(): + parser = argparse.ArgumentParser( + description="Split and join large files to work around size limitations" + ) + + subparsers = parser.add_subparsers(dest="command", help="Command to run") + + # Split command + split_parser = subparsers.add_parser("split", help="Split a file into chunks") + split_parser.add_argument("file", help="File to split") + split_parser.add_argument( + "--size", + type=int, + default=1000, + help="Size of each chunk in MB (default: 1000)", + ) + + # Join command + join_parser = subparsers.add_parser("join", help="Join chunks back into a file") + join_parser.add_argument("chunks_dir", help="Directory containing chunks") + + args = parser.parse_args() + + if args.command == "split": + split_file(args.file, args.size) + elif args.command == "join": + join_file(args.chunks_dir) + else: + parser.print_help() + + +if __name__ == "__main__": + main() diff --git a/src/medvision_ds/utils/preprocess_utils.py b/src/medvision_ds/utils/preprocess_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f7cc61dfd3ed652db8c59988cc5d8e3defd72423 --- /dev/null +++ b/src/medvision_ds/utils/preprocess_utils.py @@ -0,0 +1,503 @@ +import os +import glob +import shutil +import sys +import filecmp +import hashlib +import nibabel as nib +import numpy as np +import json +import math +from pathlib import Path + + +# NOTE: +# In a kubernetes environment, the number of CPUs available to the container may be limited by cgroups. +# This function retrieves the number of CPUs available to the container. +# DO NOT use os.cpu_count() directly, as it may return the total number of CPUs on the host machine, +def _get_cgroup_limited_cpus(): + # cgroup v1 + try: + base = Path("/sys/fs/cgroup/cpu") + q = base / "cpu.cfs_quota_us" + p = base / "cpu.cfs_period_us" + if q.exists() and p.exists(): + quota = int(q.read_text().strip()) + period = int(p.read_text().strip()) + if quota > 0 and period > 0: + return math.floor(quota / period) + except (ValueError, OSError): + pass + + # cgroup v2 + try: + line = Path("/sys/fs/cgroup/cpu.max").read_text().strip() + quota, period = line.split() + if quota != "max": + return math.floor(int(quota) / int(period)) + except (ValueError, OSError): + pass + + # fallback to host-wide CPU count + return os.cpu_count() + + +def move_folder(source_folder, destination_folder, create_dest=True): + """ + Moves a folder from source to destination. + + Args: + source_folder (str): Path to the source folder to move + destination_folder (str): Path to the destination location + create_dest (bool): Whether to create the destination parent directory if it doesn't exist + + Returns: + bool: True if successful, False otherwise + + Raises: + FileNotFoundError: If the source folder doesn't exist + """ + # Check if source folder exists + if not os.path.exists(source_folder): + raise FileNotFoundError(f"Source folder does not exist: {source_folder}") + # Create destination directory if it doesn't exist and create_dest is True + if create_dest and not os.path.exists(os.path.dirname(destination_folder)): + os.makedirs(os.path.dirname(destination_folder), exist_ok=True) + try: + # Check if destination folder exists + if os.path.exists(destination_folder): + # If destination exists, move contents + for item in os.listdir(source_folder): + s = os.path.join(source_folder, item) + d = os.path.join(destination_folder, item) + shutil.move(s, d) + else: + # If destination doesn't exist, move the entire folder + shutil.move(source_folder, destination_folder) + print(f"Successfully moved '{source_folder}' to '{destination_folder}'") + return True + except Exception as e: + print(f"Failed to move folder: {e}") + return False + + +def check_nii_header_for_img_mask(image_path, mask_path): + # Inspect the mask files + mask_nii = nib.load(mask_path) + mask_data = mask_nii.get_fdata() + mask_file_info = { + "voxel_size": tuple(round(x, 3) for x in mask_nii.header.get_zooms()), + "affine": np.round(mask_nii.affine, 3), + "orientation": nib.orientations.aff2axcodes(mask_nii.affine), + "array_size": mask_data.shape, + } + # Inspect the image files + img_nii = nib.load(image_path) + img_data = img_nii.get_fdata() + image_file_info = { + "voxel_size": tuple(round(x, 3) for x in img_nii.header.get_zooms()), + "affine": np.round(img_nii.affine, 3), + "orientation": nib.orientations.aff2axcodes(img_nii.affine), + "array_size": img_data.shape, + } + # Check if mask and image properties match + print( + f"Checking properties for the image and mask images:\nImage: {image_path}\nMask: {mask_path}" + ) + for key in mask_file_info: + if isinstance(mask_file_info[key], np.ndarray): + if not np.allclose( + mask_file_info[key], image_file_info[key], atol=1e-5, rtol=1e-3 + ): + raise ValueError( + f"\n\nMismatch in {key} between image and mask:\n" + f"Image {key}:\n{image_file_info[key]}\n" + f"Mask {key}:\n{mask_file_info[key]}\n" + ) + elif mask_file_info[key] != image_file_info[key]: + raise ValueError( + f"\n\nMismatch in {key} between image and mask:\n" + f"Image {key}:\n{image_file_info[key]}\n" + f"Mask {key}:\n{mask_file_info[key]}\n" + ) + print(f"Properties (NIfTI file header) match!\n") + + +def check_nii_header_for_img_mask_batch(image_dir, mask_dir): + """ + Check NIfTI headers for all matching image and mask pairs in given directories + + Args: + image_dir (str): Directory containing image files + mask_dir (str): Directory containing mask files + """ + # Get all nii.gz files in image directory + image_files = glob.glob(os.path.join(image_dir, "*.nii.gz")) + total_files = len(image_files) + + print(f"Found {total_files} image files. Starting header check...\n") + + for idx, image_path in enumerate(image_files, 1): + # Get corresponding mask file name + image_name = os.path.basename(image_path) + mask_path = os.path.join(mask_dir, image_name) + + print(f"Processing file {idx}/{total_files}") + + # Check if mask exists + if not os.path.exists(mask_path): + print(f"WARNING: No matching mask found for {image_name}\n") + continue + + try: + check_nii_header_for_img_mask(image_path, mask_path) + except ValueError as e: + print(f"ERROR: {str(e)}") + continue + except Exception as e: + print(f"ERROR: Unexpected error processing {image_name}: {str(e)}\n") + continue + + print("\nHeader check completed for all files!") + + +def compare_nifti_folders(folder1, folder2, check_content=False, recursive=False): + """ + Compare .nii.gz files in two folders, printing messages for files in folder1 + that don't exist in folder2. + + Args: + folder1 (str): Path to the first folder + folder2 (str): Path to the second folder + check_content (bool): If True, compare file contents, not just names + recursive (bool): If True, search subdirectories recursively + + Returns: + list: List of missing files (relative paths) + """ + + def files_are_identical(file1, file2): + """ + Check if two files have identical content using hash comparison. + + Args: + file1 (Path): Path to first file + file2 (Path): Path to second file + + Returns: + bool: True if files have identical content, False otherwise + """ + # For small files, use direct comparison + if file1.stat().st_size < 100 * 1024 * 1024: # Less than 100MB + return filecmp.cmp(file1, file2, shallow=False) + + # For larger files, compare using hashing + return get_file_hash(file1) == get_file_hash(file2) + + def get_file_hash(filepath, chunk_size=8192): + """Calculate SHA-256 hash of a file in chunks to handle large files.""" + sha256 = hashlib.sha256() + with open(filepath, "rb") as f: + while True: + data = f.read(chunk_size) + if not data: + break + sha256.update(data) + return sha256.hexdigest() + + folder1_path = Path(folder1) + folder2_path = Path(folder2) + + # Make sure both folders exist + if not folder1_path.exists(): + raise ValueError(f"Source folder does not exist: {folder1}") + if not folder2_path.exists(): + raise ValueError(f"Target folder does not exist: {folder2}") + + # Get all .nii.gz files in folder1 + pattern = "**/*.nii.gz" if recursive else "*.nii.gz" + files1 = list(folder1_path.glob(pattern)) + + missing_files = [] + + print(f"Comparing {len(files1)} .nii.gz files from {folder1} with {folder2}...") + + for file1 in files1: + # Get relative path if recursive + rel_path = file1.relative_to(folder1_path) if recursive else file1.name + file2 = folder2_path / rel_path + + if not file2.exists(): + print(f"Missing file: {rel_path}") + missing_files.append(str(rel_path)) + elif check_content and not files_are_identical(file1, file2): + print(f"Different content: {rel_path}") + missing_files.append(str(rel_path)) + + if not missing_files: + print("All files from folder1 exist in folder2") + else: + print(f"Found {len(missing_files)} missing or different files") + + return missing_files + + +def print_unique_values(nii_path, verbose=True): + """ + Print the unique values in a NIfTI (.nii.gz) file + + Args: + nii_path (str): Path to the NIfTI file + verbose (bool): If True, print additional statistics + max_display (int): Maximum number of values to display + + Returns: + numpy.ndarray: Array of unique values + """ + # Load the NIfTI file + img = nib.load(nii_path) + data = img.get_fdata() + + # Get unique values + unique_vals = np.unique(data) + + # Print results + filename = os.path.basename(nii_path) + print(f"\nUnique values in {filename}:") + print(f"Total unique values: {len(unique_vals)}") + + if verbose: + print(f"Data type: {data.dtype}") + print(f"Min value: {np.min(data)}") + print(f"Max value: {np.max(data)}") + print(f"Data shape: {data.shape}") + + # Display all unique values, regardless of the number + print(f"Values: {unique_vals}") + + return unique_vals + + +# Example for processing a directory of files +def print_unique_values_batch(directory): + """Process all .nii.gz files in a directory""" + for nii_file in Path(directory).glob("*.nii.gz"): + print_unique_values(str(nii_file)) + + +def check_noninteger_labels(folder_path, log_out_dir): + # List to store filenames with non-integer values + non_integer_files = [] + + # Get total number of files for progress tracking + total_files = sum( + 1 + for _, _, files in os.walk(folder_path) + for file in files + if file.endswith(".nii.gz") + ) + processed = 0 + + # Walk through the directory + print(f"Checking {total_files} files for non-integer labels...") + for root, dirs, files in os.walk(folder_path): + for file in files: + if file.endswith(".nii.gz"): + processed += 1 + file_path = os.path.join(root, file) + print(f" - Checking {processed}/{total_files}: {file}") + + try: + img = nib.load(file_path) + data = img.get_fdata() + unique_vals = np.unique(data) + is_all_integer = np.all(np.equal(np.mod(unique_vals, 1), 0)) + if not is_all_integer: + non_integer_files.append( + { + "filename": file, + "unique_values": unique_vals.tolist(), # Convert to list for JSON serialization + } + ) + except Exception as e: + print(f"\n\nError checking {file}: {str(e)}\n\n") + + # Print results and save to file if non-integer files found + if non_integer_files: + print(f"\nMasks with non-integer values in this folder: {folder_path}:\n") + for item in non_integer_files: + print(f"Filename: {item['filename']}") + print("Unique values found:", item["unique_values"], "\n") + + # Save to file + with open(f"{log_out_dir}/non_integer_mask_files.json", "w") as f: + json.dump(non_integer_files, f, indent=2) + + sys.exit("\n\nError: Non-integer values found in segmentation masks\n\n") + else: + print("\nAll mask files contain integer values only!\n") + + +def split_4d_nifti(input_dir, out_dir): + """ + Split 4D NIfTI files in the input directory into separate 3D files. + Automatically detects the length of the 4th dimension. + """ + # Get all .nii.gz files in the Images directory + nifti_files = glob.glob(os.path.join(input_dir, "*.nii.gz")) + + for file_path in nifti_files: + # Load the NIfTI file + img = nib.load(file_path) + data = img.get_fdata() + + # Check if it's a 4D image + if len(data.shape) != 4: + print(f"Skipping {file_path} - not a 4D image") + continue + + # Get the length of the 4th dimension + time_points = data.shape[3] + + # Create output directories if they don't exist + for i in range(1, time_points + 1): + os.makedirs(f"{out_dir}/Images-{i}", exist_ok=True) + + # Get the base filename without extension + base_name = os.path.basename(file_path).replace(".nii.gz", "") + + # Split and save each volume + for i in range(time_points): + volume = data[:, :, :, i] + new_img = nib.Nifti1Image(volume, img.affine) + output_path = os.path.join(f"{out_dir}/Images-{i+1}", f"{base_name}.nii.gz") + nib.save(new_img, output_path) + if i == time_points - 1: + print(f"Saved {output_path} (volume {i+1}/{time_points})\n") + else: + print(f"Saved {output_path} (volume {i+1}/{time_points})") + + +def process_dataset_mm(data_dirs, seg_pattern, modalities, base_suffix, replace=False): + """Generic function to process multi-modality datasets with different patterns""" + for data_dir in data_dirs: + for seg_file in glob.glob(f"{data_dir}/**/{seg_pattern}", recursive=True): + # Extract base ID + base_id = os.path.basename(seg_file).replace(base_suffix, "") + dir_name = os.path.dirname(seg_file) + + # Move segmentation file + mv_cmd = ( + shutil.move + if not replace + else lambda src, dst: shutil.move(src, dst, copy_function=shutil.copy2) + ) + os.makedirs("Masks", exist_ok=True) + mv_cmd(seg_file, f"Masks/{os.path.basename(seg_file)}") + + # Move modality files + for modality in modalities: + if "_" in base_suffix: + img_file = f"{dir_name}/{base_id}_{modality}.nii.gz" + else: + img_file = f"{dir_name}/{base_id}-{modality}.nii.gz" + + if os.path.exists(img_file): + os.makedirs(f"Images-{modality}", exist_ok=True) + mv_cmd(img_file, f"Images-{modality}/{os.path.basename(img_file)}") + else: + print(f"Warning: Missing {modality} file for {base_id}") + + +def process_dataset( + data_dirs, + seg_pattern, + base_suffix, + img_suffix=".nii.gz", + out_dir=None, + replace=False, + masks_fname="Masks", + images_fname="Images", +): + """ + Generic function to process datasets with optional arguments: output directory, file replacement + Logic: + 1. Within the folder, find segmentation files with a given pattern: + 2. Extract base ID from the segmentation file name by removing the + 3. Move the segmentation file to the /Masks folder (if provided) + 4. Find the corresponding image file by appending the to the base ID + 5. Move the image file to the /Images folder (if provided) + """ + for data_dir in data_dirs: + for seg_file in glob.glob(f"{data_dir}/**/{seg_pattern}", recursive=True): + # Extract base ID + base_id = os.path.basename(seg_file).replace(base_suffix, "") + dir_name = os.path.dirname(seg_file) + + # Move segmentation file + mv_cmd = ( + shutil.move + if not replace + else lambda src, dst: shutil.move(src, dst, copy_function=shutil.copy2) + ) + masks_dir = f"{out_dir}/{masks_fname}" if out_dir else masks_fname + os.makedirs(masks_dir, exist_ok=True) + mv_cmd(seg_file, f"{masks_dir}/{os.path.basename(seg_file)}") + + # Move image files + img_file = f"{dir_name}/{base_id}{img_suffix}" + if os.path.exists(img_file): + images_dir = f"{out_dir}/{images_fname}" if out_dir else images_fname + os.makedirs(images_dir, exist_ok=True) + mv_cmd(img_file, f"{images_dir}/{os.path.basename(img_file)}") + else: + print(f"Warning: Missing image file for {base_id}") + + +def match_and_clean_files(images_dir, masks_dir): + print( + f"Checking for matching image and mask files in {images_dir} and {masks_dir}...\n" + ) + print("Removing image files without corresponding mask files...\n") + + # Get list of image files + image_files = glob.glob(os.path.join(images_dir, "*_0000.nii.gz")) + total_images = len(image_files) + print(f"Found {total_images} image files") + + removed_count = 0 + + # Process each image file + for i, image_path in enumerate(image_files, 1): + # Extract ID from image filename + image_name = os.path.basename(image_path) + image_id = image_name.replace("_0000.nii.gz", "") + + # Construct expected mask filename + mask_path = os.path.join(masks_dir, f"{image_id}.nii.gz") + + print(f"Checking {i}/{total_images}: {image_name}") + + # Check if corresponding mask exists + if not os.path.exists(mask_path): + print(f" - Removing {image_name} - No corresponding mask found") + os.remove(image_path) + removed_count += 1 + else: + print(f" - Found matching mask for {image_name}") + + print("\nSummary:") + print(f"Total images processed: {total_images}") + print(f"Images removed: {removed_count}") + print(f"Images remaining: {total_images - removed_count}") + + +def convert_to_serializable(obj): + """Convert numpy types to Python native types for JSON serialization""" + if isinstance(obj, np.floating): + return float(obj) + elif isinstance(obj, np.integer): + return int(obj) + elif isinstance(obj, np.ndarray): + return obj.tolist() + return obj diff --git a/src/medvision_ds/utils/visualization_utils.py b/src/medvision_ds/utils/visualization_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8a7d96b5259f2c60e8174f24ba1eca0d7e632d75 --- /dev/null +++ b/src/medvision_ds/utils/visualization_utils.py @@ -0,0 +1,141 @@ +import json +import os +import glob +import matplotlib.pyplot as plt +import nibabel as nib + + +def plot_slice_with_landmarks(nii_path: str, json_path: str, fig_path: str = None): + """Plot first slice from NIfTI file and overlay landmarks from JSON file. + + Args: + nii_path (str): Path to .nii.gz file + json_path (str): Path to landmarks JSON file + fig_path (str, optional): Path to save the plot. If None, displays plot + """ + # Load NIfTI image and extract first slice + nii_img = nib.load(nii_path) + slice_data = nii_img.get_fdata()[0, :, :] + + # Load landmark coordinates from JSON + with open(json_path, "r") as f: + landmarks = json.load(f) + + # Setup visualization + plt.figure(figsize=(12, 12)) + plt.imshow( + slice_data.T, cmap="gray", origin="lower" + ) # the transpose is necessary only for visualization + + # Extract and plot landmark coordinates + x_coords = [] + y_coords = [] + for point_id, coords in landmarks.items(): + if len(coords) == 3: # Check for valid [1, x, y] format + # Note: this is definitely correct, DO NOT SWAP coords[1] and coords[2] + x_coords.append(coords[1]) + y_coords.append(coords[2]) + + # Add landmarks and labels + plt.scatter( + x_coords, + y_coords, + facecolors="#18A727", + edgecolors="black", + marker="o", + s=80, + linewidth=1.5, + ) + for i, (x, y) in enumerate(zip(x_coords, y_coords), 1): + plt.annotate( + f"$\\mathbf{{{i}}}$", + (x, y), + xytext=(2, 2), + textcoords="offset points", + color="#FE9100", + fontsize=14, + ) + + # Configure plot appearance + plt.xlabel("Anterior →", fontsize=14) + plt.ylabel("Superior →", fontsize=14) + plt.margins(0) + + # Save or display the plot + if fig_path: + plt.savefig(fig_path, bbox_inches="tight", dpi=300) + print(f"Plot saved to: {fig_path}") + else: + plt.show() + + plt.close() + + +def plot_slice_with_landmarks_batch(image_dir: str, landmark_dir: str, fig_dir: str): + """Plot all cases from given directories. + + Args: + image_dir (str): Directory containing .nii.gz files + landmark_dir (str): Directory containing landmark JSON files + fig_dir (str): Directory to save output figures + + """ + # Create output directory if it doesn't exist + os.makedirs(fig_dir, exist_ok=True) + + # Process each .nii.gz file + for nii_path in glob.glob(os.path.join(image_dir, "*.nii.gz")): + base_name = os.path.splitext(os.path.splitext(os.path.basename(nii_path))[0])[0] + json_path = os.path.join(landmark_dir, f"{base_name}.json") + fig_path = os.path.join(fig_dir, f"{base_name}.png") + + # Plot and save + if os.path.exists(json_path): + plot_slice_with_landmarks(nii_path, json_path, fig_path) + else: + print(f"Warning: No landmark file found for {base_name}") + + +def plot_2Darray_wRASinfo(img_data, slice_dim, pixel_sizes, save_path): + """Helper function to plot 2D image slices with RAS orientation info.""" + # Create visualization + img_height, img_width = img_data.shape + aspect_ratio = img_width / img_height + base_size = 10 + figsize = ( + (base_size * aspect_ratio, base_size) + if aspect_ratio > 1 + else (base_size, base_size / aspect_ratio) + ) + # Calculate aspect ratio based on pixel sizes + aspect_ratio = pixel_sizes[1] / pixel_sizes[0] + # Plot image and landmarks with correct aspect ratio + plt.figure(figsize=figsize) + + # Handle different slice orientations + if slice_dim == 0: # Sagittal + plt.imshow( + img_data.T, + cmap="gray", + origin="lower", + aspect=aspect_ratio, + ) + plt.xlabel("Anterior →", fontsize=14) + plt.ylabel("Superior →", fontsize=14) + elif slice_dim == 1: # Coronal + plt.imshow( + img_data.T, + cmap="gray", + origin="lower", + aspect=aspect_ratio, + ) + plt.xlabel("Right →", fontsize=14) + plt.ylabel("Superior →", fontsize=14) + else: # Axial + plt.imshow(img_data.T, cmap="gray", origin="lower", aspect=aspect_ratio) + plt.xlabel("Right →", fontsize=14) + plt.ylabel("Anterior →", fontsize=14) + + plt.margins(0) + plt.savefig(save_path) + plt.close() diff --git a/src/pyproject.toml b/src/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..b5c3e70c42a89a75b77a95ac43f49d3824d9c953 --- /dev/null +++ b/src/pyproject.toml @@ -0,0 +1,69 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "medvision_ds" +dynamic = ["version"] +description = "medvision_ds: a codebase for the MedVision dataset" +readme = "README.md" +requires-python = ">=3.9" +authors = [{ name = "Yongcheng Yao", email = "yc.yao@ed.ac.uk" }] +license = { text = "CC-BY-NC 4.0" } +dependencies = [ + "nibabel", + "numpy", + "SimpleITK", + "huggingface_hub[xet,cli]", + "synapseclient", + "gdown", + "gdrive", + "requests", + "scipy", + "opencv-python", + "matplotlib", + "rarfile", + "py7zr", + "scikit-image", + "pynrrd", + "tqdm", + "pandas", + "datasets==3.6.0", +] + +[project.urls] +"License" = "https://creativecommons.org/licenses/by-nc/4.0/" +"Homepage" = "https://medvision.github.io" + +[tool.setuptools] +packages = [ + "medvision_ds", + "medvision_ds.utils", + "medvision_ds.datasets", + "medvision_ds.datasets.AbdomenAtlas__1_0__Mini", + "medvision_ds.datasets.AbdomenCT_1K", + "medvision_ds.datasets.ACDC", + "medvision_ds.datasets.AMOS22", + "medvision_ds.datasets.autoPET_III", + "medvision_ds.datasets.BCV15", + "medvision_ds.datasets.BraTS24", + "medvision_ds.datasets.CAMUS", + "medvision_ds.datasets.Ceph_Biometrics_400", + "medvision_ds.datasets.CrossMoDA", + "medvision_ds.datasets.FLARE22", + "medvision_ds.datasets.FeTA24", + "medvision_ds.datasets.HNTSMRG24", + "medvision_ds.datasets.ISLES24", + "medvision_ds.datasets.KiPA22", + "medvision_ds.datasets.KiTS23", + "medvision_ds.datasets.MSD", + "medvision_ds.datasets.OAIZIB_CM", + "medvision_ds.datasets.SKM_TEA", + "medvision_ds.datasets.ToothFairy2", + "medvision_ds.datasets.TopCoW24", + "medvision_ds.datasets.TotalSegmentator", +] +package-dir = { "" = "." } + +[tool.setuptools.dynamic] +version = { attr = "medvision_ds.__version__" }