Datasets:
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Parent(s):
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init release
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- .gitignore +176 -0
- Datasets/ACDC.zip +3 -0
- Datasets/AMOS22.zip +3 -0
- Datasets/AbdomenAtlas1.0Mini.zip +3 -0
- Datasets/AbdomenCT-1K.zip +3 -0
- Datasets/BCV15.zip +3 -0
- Datasets/BraTS24.zip +3 -0
- Datasets/CAMUS.zip +3 -0
- Datasets/Ceph-Biometrics-400.zip +3 -0
- Datasets/CrossMoDA.zip +3 -0
- Datasets/FLARE22.zip +3 -0
- Datasets/FeTA24.zip +3 -0
- Datasets/HNTSMRG24.zip +3 -0
- Datasets/ISLES24.zip +3 -0
- Datasets/KiPA22.zip +3 -0
- Datasets/KiTS23.zip +3 -0
- Datasets/MSD.zip +3 -0
- Datasets/OAIZIB-CM.zip +3 -0
- Datasets/SKM-TEA.zip +3 -0
- Datasets/ToothFairy2.zip +3 -0
- Datasets/TopCoW24.zip +3 -0
- Datasets/TotalSegmentator.zip +3 -0
- Datasets/autoPET-III.zip +3 -0
- LICENSE +1 -0
- MedVision.py +0 -0
- README.md +42 -3
- info/ConfigurationsList_All.csv +820 -0
- info/ConfigurationsList_Test.csv +410 -0
- info/ConfigurationsList_Train.csv +410 -0
- src/medvision_ds/__init__.py +6 -0
- src/medvision_ds/__version__.py +1 -0
- src/medvision_ds/datasets/ACDC/__init__.py +0 -0
- src/medvision_ds/datasets/ACDC/download_fast.py +102 -0
- src/medvision_ds/datasets/ACDC/download_raw.py +122 -0
- src/medvision_ds/datasets/ACDC/preprocess_detection.py +128 -0
- src/medvision_ds/datasets/ACDC/preprocess_segmentation.py +128 -0
- src/medvision_ds/datasets/AMOS22/__init__.py +0 -0
- src/medvision_ds/datasets/AMOS22/download.py +130 -0
- src/medvision_ds/datasets/AMOS22/preprocess_detection.py +152 -0
- src/medvision_ds/datasets/AMOS22/preprocess_segmentation.py +152 -0
- src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/__init__.py +0 -0
- src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/download_raw.py +161 -0
- src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/preprocess_detection.py +136 -0
- src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/preprocess_segmentation.py +137 -0
- src/medvision_ds/datasets/AbdomenCT_1K/__init__.py +0 -0
- src/medvision_ds/datasets/AbdomenCT_1K/download.py +127 -0
- src/medvision_ds/datasets/AbdomenCT_1K/preprocess_detection.py +131 -0
- src/medvision_ds/datasets/AbdomenCT_1K/preprocess_segmentation.py +131 -0
- src/medvision_ds/datasets/BCV15/__init__.py +0 -0
- src/medvision_ds/datasets/BCV15/download.py +147 -0
.gitignore
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# Created by https://www.toptal.com/developers/gitignore/api/python
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# Edit at https://www.toptal.com/developers/gitignore?templates=python
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+
### Python ###
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+
# Byte-compiled / optimized / DLL files
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+
__pycache__/
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*.py[cod]
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*$py.class
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+
# C extensions
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+
*.so
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# Distribution / packaging
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+
.Python
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+
build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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+
MANIFEST
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+
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+
# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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| 63 |
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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| 102 |
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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| 109 |
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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| 111 |
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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### Python Patch ###
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# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
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poetry.toml
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# ruff
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.ruff_cache/
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# LSP config files
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pyrightconfig.json
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# End of https://www.toptal.com/developers/gitignore/api/python
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Datasets/ACDC.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:dcb92b84c54fdb9702ed2c893365a209bcdb241c63dfa0dcb13dbc8d6d7ccb80
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size 3782824
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Datasets/AMOS22.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:818e3e4e0555cd03968f3c19b8b536cff7755376e928c2ee2812358f8b37ae81
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size 52344969
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Datasets/AbdomenAtlas1.0Mini.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:f2697cd5db6eb4d68550134df1246ea41fa517ec7f2be392d4aca3d3ec316a48
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size 720784876
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Datasets/AbdomenCT-1K.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:11f76256950db2c51569ecd2813abc51c8f1d802b604357620b0e6e308ed6d18
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size 76290869
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Datasets/BCV15.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:844636afe8f09bef0edfb401593afb8132bd42f3a8bdbc5969d52398b56303ac
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size 5484512
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Datasets/BraTS24.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:c4c49974a118e4f778bdfd5dffea0d07f8468d705665dd74485b9d0695705577
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size 1164911581
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Datasets/CAMUS.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:0cea19ec40b51a72327256ff5491166c887dcfd9359d037e9aae562a9f1f2410
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size 54348626
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Datasets/Ceph-Biometrics-400.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:9b053c9f66c9a275ea8611559a5657d43197e098ea44ee99242d6004b9f96047
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size 132160
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Datasets/CrossMoDA.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:ea9d0e1fb1a33fc5100d96e24ac80a766d9e4ea15043c8085976c95e2adf3e2d
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size 709309
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Datasets/FLARE22.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:f3dd6a84a0c5fa799f4f8aaf5da1163ca85a8bb8df50829b2cd4e54cd4420e6d
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size 7714459
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Datasets/FeTA24.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:b9ced4e1e90f4a2864c88d76caf600e01e3263b6391fcff0955632198e6e4550
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size 17351418
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Datasets/HNTSMRG24.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:f0aba71c13c338c471f418af95032fdefdd00089bce0bade41db32ac73bdf6d6
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size 365538926
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Datasets/ISLES24.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:d9468c053e8ac7e208a60946c1fc7c8f7883671ef2ea927ae2c777f987697ade
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size 7263795
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Datasets/KiPA22.zip
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:16d9df6be1c976969bcfad61697cd218994521fae1971bd1bb89e69a6a41a95a
|
| 3 |
+
size 533364627
|
Datasets/KiTS23.zip
ADDED
|
@@ -0,0 +1,3 @@
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|
|
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|
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|
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|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:0c49dce5d5009c373188df83511163a57063d468b4364482514b1941a234cca6
|
| 3 |
+
size 2247706597
|
Datasets/MSD.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:63718a4752fe44c188513d167e5637a517b8cfaa670facb14b2fc428889f6c34
|
| 3 |
+
size 1554507726
|
Datasets/OAIZIB-CM.zip
ADDED
|
@@ -0,0 +1,3 @@
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:dd11ef20d5c33b98d9c2a1041062d4e07426732a75c81042d0cf8789efe9507d
|
| 3 |
+
size 53329224
|
Datasets/SKM-TEA.zip
ADDED
|
@@ -0,0 +1,3 @@
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:f633727ec1bbbe68d5ee294b2d81b76fe1fc6b8a3070b2f2f806489ba370002f
|
| 3 |
+
size 28195237
|
Datasets/ToothFairy2.zip
ADDED
|
@@ -0,0 +1,3 @@
|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:cc343d0f51aaae299b1ec5a25220512b972a71e8d59103b834d06af0a9bed472
|
| 3 |
+
size 108617082
|
Datasets/TopCoW24.zip
ADDED
|
@@ -0,0 +1,3 @@
|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:b346b99b28a7add01f6c3efbd13adfa7f12124df5a78bd8c1c7908920fe2afc6
|
| 3 |
+
size 10380429
|
Datasets/TotalSegmentator.zip
ADDED
|
@@ -0,0 +1,3 @@
|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1b823e73589521e3a31189bf18726695b04363267c945e5ed3a5723cbcb38010
|
| 3 |
+
size 830561524
|
Datasets/autoPET-III.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:67c63f01aa85155c8535fde62a6895687465faa40420b7e19d0913fdc088ad08
|
| 3 |
+
size 180238799
|
LICENSE
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
Data and code are released under CC-BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).
|
MedVision.py
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
README.md
CHANGED
|
@@ -1,3 +1,42 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
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|
| 1 |
+
# About
|
| 2 |
+
**MedVision**, a large-scale, multi-anatomy, multi-modality dataset for quantitative medical image analysis.
|
| 3 |
+
|
| 4 |
+
# News
|
| 5 |
+
- [Oct 8, 2025] 🚀 Release **MedVision** dataset v1.0.0
|
| 6 |
+
|
| 7 |
+
# Requirement
|
| 8 |
+
📝 Note: `trust_remote_code` is no longer supported in datasets>=4.0.0, install `dataset` with `pip install datasets==3.6.0`
|
| 9 |
+
|
| 10 |
+
# Use
|
| 11 |
+
```python
|
| 12 |
+
import os
|
| 13 |
+
from datasets import load_dataset, config
|
| 14 |
+
|
| 15 |
+
# Set data folder
|
| 16 |
+
os.environ["MedVision_DATA_DIR"] = <your/data/folder>
|
| 17 |
+
|
| 18 |
+
# Pick a dataset config name and split
|
| 19 |
+
config = <config-name>
|
| 20 |
+
split_name = "test", # use "test" for testing set config; use "train" for training set config
|
| 21 |
+
|
| 22 |
+
# Get dataset
|
| 23 |
+
ds = load_dataset(
|
| 24 |
+
"YongchengYAO/MedVision",
|
| 25 |
+
name=config,
|
| 26 |
+
trust_remote_code=True,
|
| 27 |
+
split=split_name,
|
| 28 |
+
)
|
| 29 |
+
```
|
| 30 |
+
📝 List of config names in `info/`
|
| 31 |
+
|
| 32 |
+
# Environment Variables
|
| 33 |
+
```bash
|
| 34 |
+
# Set where data will be saved, requires ~1T for the complete dataset
|
| 35 |
+
export MedVision_DATA_DIR=<your/data/folder>
|
| 36 |
+
|
| 37 |
+
# Force download and process raw images, default to "False"
|
| 38 |
+
export MedVision_FORCE_DOWNLOAD_DATA="False"
|
| 39 |
+
|
| 40 |
+
# Force install dataset codebase, default to "False"
|
| 41 |
+
export MedVision_FORCE_INSTALL_CODE="False"
|
| 42 |
+
```
|
info/ConfigurationsList_All.csv
ADDED
|
@@ -0,0 +1,820 @@
|
|
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|
| 1 |
+
AbdomenAtlas1.0Mini_MaskSize_Task01_Sagittal_Train
|
| 2 |
+
AbdomenAtlas1.0Mini_MaskSize_Task01_Sagittal_Test
|
| 3 |
+
AbdomenAtlas1.0Mini_MaskSize_Task01_Coronal_Train
|
| 4 |
+
AbdomenAtlas1.0Mini_MaskSize_Task01_Coronal_Test
|
| 5 |
+
AbdomenAtlas1.0Mini_MaskSize_Task01_Axial_Train
|
| 6 |
+
AbdomenAtlas1.0Mini_MaskSize_Task01_Axial_Test
|
| 7 |
+
AbdomenAtlas1.0Mini_BoxSize_Task01_Sagittal_Train
|
| 8 |
+
AbdomenAtlas1.0Mini_BoxSize_Task01_Sagittal_Test
|
| 9 |
+
AbdomenAtlas1.0Mini_BoxSize_Task01_Coronal_Train
|
| 10 |
+
AbdomenAtlas1.0Mini_BoxSize_Task01_Coronal_Test
|
| 11 |
+
AbdomenAtlas1.0Mini_BoxSize_Task01_Axial_Train
|
| 12 |
+
AbdomenAtlas1.0Mini_BoxSize_Task01_Axial_Test
|
| 13 |
+
AbdomenCT-1K_MaskSize_Task01_Sagittal_Train
|
| 14 |
+
AbdomenCT-1K_MaskSize_Task01_Sagittal_Test
|
| 15 |
+
AbdomenCT-1K_MaskSize_Task01_Coronal_Train
|
| 16 |
+
AbdomenCT-1K_MaskSize_Task01_Coronal_Test
|
| 17 |
+
AbdomenCT-1K_MaskSize_Task01_Axial_Train
|
| 18 |
+
AbdomenCT-1K_MaskSize_Task01_Axial_Test
|
| 19 |
+
AbdomenCT-1K_BoxSize_Task01_Sagittal_Train
|
| 20 |
+
AbdomenCT-1K_BoxSize_Task01_Sagittal_Test
|
| 21 |
+
AbdomenCT-1K_BoxSize_Task01_Coronal_Train
|
| 22 |
+
AbdomenCT-1K_BoxSize_Task01_Coronal_Test
|
| 23 |
+
AbdomenCT-1K_BoxSize_Task01_Axial_Train
|
| 24 |
+
AbdomenCT-1K_BoxSize_Task01_Axial_Test
|
| 25 |
+
ACDC_MaskSize_Task01_Sagittal_Train
|
| 26 |
+
ACDC_MaskSize_Task01_Sagittal_Test
|
| 27 |
+
ACDC_MaskSize_Task01_Coronal_Train
|
| 28 |
+
ACDC_MaskSize_Task01_Coronal_Test
|
| 29 |
+
ACDC_MaskSize_Task01_Axial_Train
|
| 30 |
+
ACDC_MaskSize_Task01_Axial_Test
|
| 31 |
+
ACDC_BoxSize_Task01_Sagittal_Train
|
| 32 |
+
ACDC_BoxSize_Task01_Sagittal_Test
|
| 33 |
+
ACDC_BoxSize_Task01_Coronal_Train
|
| 34 |
+
ACDC_BoxSize_Task01_Coronal_Test
|
| 35 |
+
ACDC_BoxSize_Task01_Axial_Train
|
| 36 |
+
ACDC_BoxSize_Task01_Axial_Test
|
| 37 |
+
AMOS22_MaskSize_Task01_Sagittal_Train
|
| 38 |
+
AMOS22_MaskSize_Task01_Sagittal_Test
|
| 39 |
+
AMOS22_MaskSize_Task01_Coronal_Train
|
| 40 |
+
AMOS22_MaskSize_Task01_Coronal_Test
|
| 41 |
+
AMOS22_MaskSize_Task01_Axial_Train
|
| 42 |
+
AMOS22_MaskSize_Task01_Axial_Test
|
| 43 |
+
AMOS22_MaskSize_Task02_Sagittal_Train
|
| 44 |
+
AMOS22_MaskSize_Task02_Sagittal_Test
|
| 45 |
+
AMOS22_MaskSize_Task02_Coronal_Train
|
| 46 |
+
AMOS22_MaskSize_Task02_Coronal_Test
|
| 47 |
+
AMOS22_MaskSize_Task02_Axial_Train
|
| 48 |
+
AMOS22_MaskSize_Task02_Axial_Test
|
| 49 |
+
AMOS22_BoxSize_Task01_Sagittal_Train
|
| 50 |
+
AMOS22_BoxSize_Task01_Sagittal_Test
|
| 51 |
+
AMOS22_BoxSize_Task01_Coronal_Train
|
| 52 |
+
AMOS22_BoxSize_Task01_Coronal_Test
|
| 53 |
+
AMOS22_BoxSize_Task01_Axial_Train
|
| 54 |
+
AMOS22_BoxSize_Task01_Axial_Test
|
| 55 |
+
AMOS22_BoxSize_Task02_Sagittal_Train
|
| 56 |
+
AMOS22_BoxSize_Task02_Sagittal_Test
|
| 57 |
+
AMOS22_BoxSize_Task02_Coronal_Train
|
| 58 |
+
AMOS22_BoxSize_Task02_Coronal_Test
|
| 59 |
+
AMOS22_BoxSize_Task02_Axial_Train
|
| 60 |
+
AMOS22_BoxSize_Task02_Axial_Test
|
| 61 |
+
autoPET-III_MaskSize_Task01_Sagittal_Train
|
| 62 |
+
autoPET-III_MaskSize_Task01_Sagittal_Test
|
| 63 |
+
autoPET-III_MaskSize_Task01_Coronal_Train
|
| 64 |
+
autoPET-III_MaskSize_Task01_Coronal_Test
|
| 65 |
+
autoPET-III_MaskSize_Task01_Axial_Train
|
| 66 |
+
autoPET-III_MaskSize_Task01_Axial_Test
|
| 67 |
+
autoPET-III_MaskSize_Task02_Sagittal_Train
|
| 68 |
+
autoPET-III_MaskSize_Task02_Sagittal_Test
|
| 69 |
+
autoPET-III_MaskSize_Task02_Coronal_Train
|
| 70 |
+
autoPET-III_MaskSize_Task02_Coronal_Test
|
| 71 |
+
autoPET-III_MaskSize_Task02_Axial_Train
|
| 72 |
+
autoPET-III_MaskSize_Task02_Axial_Test
|
| 73 |
+
autoPET-III_BoxSize_Task01_Sagittal_Train
|
| 74 |
+
autoPET-III_BoxSize_Task01_Sagittal_Test
|
| 75 |
+
autoPET-III_BoxSize_Task01_Coronal_Train
|
| 76 |
+
autoPET-III_BoxSize_Task01_Coronal_Test
|
| 77 |
+
autoPET-III_BoxSize_Task01_Axial_Train
|
| 78 |
+
autoPET-III_BoxSize_Task01_Axial_Test
|
| 79 |
+
autoPET-III_BoxSize_Task02_Sagittal_Train
|
| 80 |
+
autoPET-III_BoxSize_Task02_Sagittal_Test
|
| 81 |
+
autoPET-III_BoxSize_Task02_Coronal_Train
|
| 82 |
+
autoPET-III_BoxSize_Task02_Coronal_Test
|
| 83 |
+
autoPET-III_BoxSize_Task02_Axial_Train
|
| 84 |
+
autoPET-III_BoxSize_Task02_Axial_Test
|
| 85 |
+
autoPET-III_TumorLesionSize_Task01_Sagittal_Train
|
| 86 |
+
autoPET-III_TumorLesionSize_Task01_Sagittal_Test
|
| 87 |
+
autoPET-III_TumorLesionSize_Task01_Coronal_Train
|
| 88 |
+
autoPET-III_TumorLesionSize_Task01_Coronal_Test
|
| 89 |
+
autoPET-III_TumorLesionSize_Task01_Axial_Train
|
| 90 |
+
autoPET-III_TumorLesionSize_Task01_Axial_Test
|
| 91 |
+
BCV15_MaskSize_Task01_Sagittal_Train
|
| 92 |
+
BCV15_MaskSize_Task01_Sagittal_Test
|
| 93 |
+
BCV15_MaskSize_Task01_Coronal_Train
|
| 94 |
+
BCV15_MaskSize_Task01_Coronal_Test
|
| 95 |
+
BCV15_MaskSize_Task01_Axial_Train
|
| 96 |
+
BCV15_MaskSize_Task01_Axial_Test
|
| 97 |
+
BCV15_MaskSize_Task02_Sagittal_Train
|
| 98 |
+
BCV15_MaskSize_Task02_Sagittal_Test
|
| 99 |
+
BCV15_MaskSize_Task02_Coronal_Train
|
| 100 |
+
BCV15_MaskSize_Task02_Coronal_Test
|
| 101 |
+
BCV15_MaskSize_Task02_Axial_Train
|
| 102 |
+
BCV15_MaskSize_Task02_Axial_Test
|
| 103 |
+
BCV15_BoxSize_Task01_Sagittal_Train
|
| 104 |
+
BCV15_BoxSize_Task01_Sagittal_Test
|
| 105 |
+
BCV15_BoxSize_Task01_Coronal_Train
|
| 106 |
+
BCV15_BoxSize_Task01_Coronal_Test
|
| 107 |
+
BCV15_BoxSize_Task01_Axial_Train
|
| 108 |
+
BCV15_BoxSize_Task01_Axial_Test
|
| 109 |
+
BCV15_BoxSize_Task02_Sagittal_Train
|
| 110 |
+
BCV15_BoxSize_Task02_Sagittal_Test
|
| 111 |
+
BCV15_BoxSize_Task02_Coronal_Train
|
| 112 |
+
BCV15_BoxSize_Task02_Coronal_Test
|
| 113 |
+
BCV15_BoxSize_Task02_Axial_Train
|
| 114 |
+
BCV15_BoxSize_Task02_Axial_Test
|
| 115 |
+
BraTS24_MaskSize_Task01_Sagittal_Train
|
| 116 |
+
BraTS24_MaskSize_Task01_Sagittal_Test
|
| 117 |
+
BraTS24_MaskSize_Task01_Coronal_Train
|
| 118 |
+
BraTS24_MaskSize_Task01_Coronal_Test
|
| 119 |
+
BraTS24_MaskSize_Task01_Axial_Train
|
| 120 |
+
BraTS24_MaskSize_Task01_Axial_Test
|
| 121 |
+
BraTS24_MaskSize_Task02_Sagittal_Train
|
| 122 |
+
BraTS24_MaskSize_Task02_Sagittal_Test
|
| 123 |
+
BraTS24_MaskSize_Task02_Coronal_Train
|
| 124 |
+
BraTS24_MaskSize_Task02_Coronal_Test
|
| 125 |
+
BraTS24_MaskSize_Task02_Axial_Train
|
| 126 |
+
BraTS24_MaskSize_Task02_Axial_Test
|
| 127 |
+
BraTS24_MaskSize_Task03_Sagittal_Train
|
| 128 |
+
BraTS24_MaskSize_Task03_Sagittal_Test
|
| 129 |
+
BraTS24_MaskSize_Task03_Coronal_Train
|
| 130 |
+
BraTS24_MaskSize_Task03_Coronal_Test
|
| 131 |
+
BraTS24_MaskSize_Task03_Axial_Train
|
| 132 |
+
BraTS24_MaskSize_Task03_Axial_Test
|
| 133 |
+
BraTS24_MaskSize_Task04_Sagittal_Train
|
| 134 |
+
BraTS24_MaskSize_Task04_Sagittal_Test
|
| 135 |
+
BraTS24_MaskSize_Task04_Coronal_Train
|
| 136 |
+
BraTS24_MaskSize_Task04_Coronal_Test
|
| 137 |
+
BraTS24_MaskSize_Task04_Axial_Train
|
| 138 |
+
BraTS24_MaskSize_Task04_Axial_Test
|
| 139 |
+
BraTS24_MaskSize_Task05_Sagittal_Train
|
| 140 |
+
BraTS24_MaskSize_Task05_Sagittal_Test
|
| 141 |
+
BraTS24_MaskSize_Task05_Coronal_Train
|
| 142 |
+
BraTS24_MaskSize_Task05_Coronal_Test
|
| 143 |
+
BraTS24_MaskSize_Task05_Axial_Train
|
| 144 |
+
BraTS24_MaskSize_Task05_Axial_Test
|
| 145 |
+
BraTS24_MaskSize_Task06_Sagittal_Train
|
| 146 |
+
BraTS24_MaskSize_Task06_Sagittal_Test
|
| 147 |
+
BraTS24_MaskSize_Task06_Coronal_Train
|
| 148 |
+
BraTS24_MaskSize_Task06_Coronal_Test
|
| 149 |
+
BraTS24_MaskSize_Task06_Axial_Train
|
| 150 |
+
BraTS24_MaskSize_Task06_Axial_Test
|
| 151 |
+
BraTS24_MaskSize_Task07_Sagittal_Train
|
| 152 |
+
BraTS24_MaskSize_Task07_Sagittal_Test
|
| 153 |
+
BraTS24_MaskSize_Task07_Coronal_Train
|
| 154 |
+
BraTS24_MaskSize_Task07_Coronal_Test
|
| 155 |
+
BraTS24_MaskSize_Task07_Axial_Train
|
| 156 |
+
BraTS24_MaskSize_Task07_Axial_Test
|
| 157 |
+
BraTS24_MaskSize_Task08_Sagittal_Train
|
| 158 |
+
BraTS24_MaskSize_Task08_Sagittal_Test
|
| 159 |
+
BraTS24_MaskSize_Task08_Coronal_Train
|
| 160 |
+
BraTS24_MaskSize_Task08_Coronal_Test
|
| 161 |
+
BraTS24_MaskSize_Task08_Axial_Train
|
| 162 |
+
BraTS24_MaskSize_Task08_Axial_Test
|
| 163 |
+
BraTS24_MaskSize_Task09_Sagittal_Train
|
| 164 |
+
BraTS24_MaskSize_Task09_Sagittal_Test
|
| 165 |
+
BraTS24_MaskSize_Task09_Coronal_Train
|
| 166 |
+
BraTS24_MaskSize_Task09_Coronal_Test
|
| 167 |
+
BraTS24_MaskSize_Task09_Axial_Train
|
| 168 |
+
BraTS24_MaskSize_Task09_Axial_Test
|
| 169 |
+
BraTS24_MaskSize_Task10_Sagittal_Train
|
| 170 |
+
BraTS24_MaskSize_Task10_Sagittal_Test
|
| 171 |
+
BraTS24_MaskSize_Task10_Coronal_Train
|
| 172 |
+
BraTS24_MaskSize_Task10_Coronal_Test
|
| 173 |
+
BraTS24_MaskSize_Task10_Axial_Train
|
| 174 |
+
BraTS24_MaskSize_Task10_Axial_Test
|
| 175 |
+
BraTS24_MaskSize_Task11_Sagittal_Train
|
| 176 |
+
BraTS24_MaskSize_Task11_Sagittal_Test
|
| 177 |
+
BraTS24_MaskSize_Task11_Coronal_Train
|
| 178 |
+
BraTS24_MaskSize_Task11_Coronal_Test
|
| 179 |
+
BraTS24_MaskSize_Task11_Axial_Train
|
| 180 |
+
BraTS24_MaskSize_Task11_Axial_Test
|
| 181 |
+
BraTS24_MaskSize_Task12_Sagittal_Train
|
| 182 |
+
BraTS24_MaskSize_Task12_Sagittal_Test
|
| 183 |
+
BraTS24_MaskSize_Task12_Coronal_Train
|
| 184 |
+
BraTS24_MaskSize_Task12_Coronal_Test
|
| 185 |
+
BraTS24_MaskSize_Task12_Axial_Train
|
| 186 |
+
BraTS24_MaskSize_Task12_Axial_Test
|
| 187 |
+
BraTS24_MaskSize_Task13_Sagittal_Train
|
| 188 |
+
BraTS24_MaskSize_Task13_Sagittal_Test
|
| 189 |
+
BraTS24_MaskSize_Task13_Coronal_Train
|
| 190 |
+
BraTS24_MaskSize_Task13_Coronal_Test
|
| 191 |
+
BraTS24_MaskSize_Task13_Axial_Train
|
| 192 |
+
BraTS24_MaskSize_Task13_Axial_Test
|
| 193 |
+
BraTS24_BoxSize_Task01_Sagittal_Train
|
| 194 |
+
BraTS24_BoxSize_Task01_Sagittal_Test
|
| 195 |
+
BraTS24_BoxSize_Task01_Coronal_Train
|
| 196 |
+
BraTS24_BoxSize_Task01_Coronal_Test
|
| 197 |
+
BraTS24_BoxSize_Task01_Axial_Train
|
| 198 |
+
BraTS24_BoxSize_Task01_Axial_Test
|
| 199 |
+
BraTS24_BoxSize_Task02_Sagittal_Train
|
| 200 |
+
BraTS24_BoxSize_Task02_Sagittal_Test
|
| 201 |
+
BraTS24_BoxSize_Task02_Coronal_Train
|
| 202 |
+
BraTS24_BoxSize_Task02_Coronal_Test
|
| 203 |
+
BraTS24_BoxSize_Task02_Axial_Train
|
| 204 |
+
BraTS24_BoxSize_Task02_Axial_Test
|
| 205 |
+
BraTS24_BoxSize_Task03_Sagittal_Train
|
| 206 |
+
BraTS24_BoxSize_Task03_Sagittal_Test
|
| 207 |
+
BraTS24_BoxSize_Task03_Coronal_Train
|
| 208 |
+
BraTS24_BoxSize_Task03_Coronal_Test
|
| 209 |
+
BraTS24_BoxSize_Task03_Axial_Train
|
| 210 |
+
BraTS24_BoxSize_Task03_Axial_Test
|
| 211 |
+
BraTS24_BoxSize_Task04_Sagittal_Train
|
| 212 |
+
BraTS24_BoxSize_Task04_Sagittal_Test
|
| 213 |
+
BraTS24_BoxSize_Task04_Coronal_Train
|
| 214 |
+
BraTS24_BoxSize_Task04_Coronal_Test
|
| 215 |
+
BraTS24_BoxSize_Task04_Axial_Train
|
| 216 |
+
BraTS24_BoxSize_Task04_Axial_Test
|
| 217 |
+
BraTS24_BoxSize_Task05_Sagittal_Train
|
| 218 |
+
BraTS24_BoxSize_Task05_Sagittal_Test
|
| 219 |
+
BraTS24_BoxSize_Task05_Coronal_Train
|
| 220 |
+
BraTS24_BoxSize_Task05_Coronal_Test
|
| 221 |
+
BraTS24_BoxSize_Task05_Axial_Train
|
| 222 |
+
BraTS24_BoxSize_Task05_Axial_Test
|
| 223 |
+
BraTS24_BoxSize_Task06_Sagittal_Train
|
| 224 |
+
BraTS24_BoxSize_Task06_Sagittal_Test
|
| 225 |
+
BraTS24_BoxSize_Task06_Coronal_Train
|
| 226 |
+
BraTS24_BoxSize_Task06_Coronal_Test
|
| 227 |
+
BraTS24_BoxSize_Task06_Axial_Train
|
| 228 |
+
BraTS24_BoxSize_Task06_Axial_Test
|
| 229 |
+
BraTS24_BoxSize_Task07_Sagittal_Train
|
| 230 |
+
BraTS24_BoxSize_Task07_Sagittal_Test
|
| 231 |
+
BraTS24_BoxSize_Task07_Coronal_Train
|
| 232 |
+
BraTS24_BoxSize_Task07_Coronal_Test
|
| 233 |
+
BraTS24_BoxSize_Task07_Axial_Train
|
| 234 |
+
BraTS24_BoxSize_Task07_Axial_Test
|
| 235 |
+
BraTS24_BoxSize_Task08_Sagittal_Train
|
| 236 |
+
BraTS24_BoxSize_Task08_Sagittal_Test
|
| 237 |
+
BraTS24_BoxSize_Task08_Coronal_Train
|
| 238 |
+
BraTS24_BoxSize_Task08_Coronal_Test
|
| 239 |
+
BraTS24_BoxSize_Task08_Axial_Train
|
| 240 |
+
BraTS24_BoxSize_Task08_Axial_Test
|
| 241 |
+
BraTS24_BoxSize_Task09_Sagittal_Train
|
| 242 |
+
BraTS24_BoxSize_Task09_Sagittal_Test
|
| 243 |
+
BraTS24_BoxSize_Task09_Coronal_Train
|
| 244 |
+
BraTS24_BoxSize_Task09_Coronal_Test
|
| 245 |
+
BraTS24_BoxSize_Task09_Axial_Train
|
| 246 |
+
BraTS24_BoxSize_Task09_Axial_Test
|
| 247 |
+
BraTS24_BoxSize_Task10_Sagittal_Train
|
| 248 |
+
BraTS24_BoxSize_Task10_Sagittal_Test
|
| 249 |
+
BraTS24_BoxSize_Task10_Coronal_Train
|
| 250 |
+
BraTS24_BoxSize_Task10_Coronal_Test
|
| 251 |
+
BraTS24_BoxSize_Task10_Axial_Train
|
| 252 |
+
BraTS24_BoxSize_Task10_Axial_Test
|
| 253 |
+
BraTS24_BoxSize_Task11_Sagittal_Train
|
| 254 |
+
BraTS24_BoxSize_Task11_Sagittal_Test
|
| 255 |
+
BraTS24_BoxSize_Task11_Coronal_Train
|
| 256 |
+
BraTS24_BoxSize_Task11_Coronal_Test
|
| 257 |
+
BraTS24_BoxSize_Task11_Axial_Train
|
| 258 |
+
BraTS24_BoxSize_Task11_Axial_Test
|
| 259 |
+
BraTS24_BoxSize_Task12_Sagittal_Train
|
| 260 |
+
BraTS24_BoxSize_Task12_Sagittal_Test
|
| 261 |
+
BraTS24_BoxSize_Task12_Coronal_Train
|
| 262 |
+
BraTS24_BoxSize_Task12_Coronal_Test
|
| 263 |
+
BraTS24_BoxSize_Task12_Axial_Train
|
| 264 |
+
BraTS24_BoxSize_Task12_Axial_Test
|
| 265 |
+
BraTS24_BoxSize_Task13_Sagittal_Train
|
| 266 |
+
BraTS24_BoxSize_Task13_Sagittal_Test
|
| 267 |
+
BraTS24_BoxSize_Task13_Coronal_Train
|
| 268 |
+
BraTS24_BoxSize_Task13_Coronal_Test
|
| 269 |
+
BraTS24_BoxSize_Task13_Axial_Train
|
| 270 |
+
BraTS24_BoxSize_Task13_Axial_Test
|
| 271 |
+
BraTS24_TumorLesionSize_Task01_Sagittal_Train
|
| 272 |
+
BraTS24_TumorLesionSize_Task01_Sagittal_Test
|
| 273 |
+
BraTS24_TumorLesionSize_Task01_Coronal_Train
|
| 274 |
+
BraTS24_TumorLesionSize_Task01_Coronal_Test
|
| 275 |
+
BraTS24_TumorLesionSize_Task01_Axial_Train
|
| 276 |
+
BraTS24_TumorLesionSize_Task01_Axial_Test
|
| 277 |
+
BraTS24_TumorLesionSize_Task02_Sagittal_Train
|
| 278 |
+
BraTS24_TumorLesionSize_Task02_Sagittal_Test
|
| 279 |
+
BraTS24_TumorLesionSize_Task02_Coronal_Train
|
| 280 |
+
BraTS24_TumorLesionSize_Task02_Coronal_Test
|
| 281 |
+
BraTS24_TumorLesionSize_Task02_Axial_Train
|
| 282 |
+
BraTS24_TumorLesionSize_Task02_Axial_Test
|
| 283 |
+
BraTS24_TumorLesionSize_Task03_Sagittal_Train
|
| 284 |
+
BraTS24_TumorLesionSize_Task03_Sagittal_Test
|
| 285 |
+
BraTS24_TumorLesionSize_Task03_Coronal_Train
|
| 286 |
+
BraTS24_TumorLesionSize_Task03_Coronal_Test
|
| 287 |
+
BraTS24_TumorLesionSize_Task03_Axial_Train
|
| 288 |
+
BraTS24_TumorLesionSize_Task03_Axial_Test
|
| 289 |
+
BraTS24_TumorLesionSize_Task04_Sagittal_Train
|
| 290 |
+
BraTS24_TumorLesionSize_Task04_Sagittal_Test
|
| 291 |
+
BraTS24_TumorLesionSize_Task04_Coronal_Train
|
| 292 |
+
BraTS24_TumorLesionSize_Task04_Coronal_Test
|
| 293 |
+
BraTS24_TumorLesionSize_Task04_Axial_Train
|
| 294 |
+
BraTS24_TumorLesionSize_Task04_Axial_Test
|
| 295 |
+
BraTS24_TumorLesionSize_Task05_Sagittal_Train
|
| 296 |
+
BraTS24_TumorLesionSize_Task05_Sagittal_Test
|
| 297 |
+
BraTS24_TumorLesionSize_Task05_Coronal_Train
|
| 298 |
+
BraTS24_TumorLesionSize_Task05_Coronal_Test
|
| 299 |
+
BraTS24_TumorLesionSize_Task05_Axial_Train
|
| 300 |
+
BraTS24_TumorLesionSize_Task05_Axial_Test
|
| 301 |
+
BraTS24_TumorLesionSize_Task06_Sagittal_Train
|
| 302 |
+
BraTS24_TumorLesionSize_Task06_Sagittal_Test
|
| 303 |
+
BraTS24_TumorLesionSize_Task06_Coronal_Train
|
| 304 |
+
BraTS24_TumorLesionSize_Task06_Coronal_Test
|
| 305 |
+
BraTS24_TumorLesionSize_Task06_Axial_Train
|
| 306 |
+
BraTS24_TumorLesionSize_Task06_Axial_Test
|
| 307 |
+
BraTS24_TumorLesionSize_Task07_Sagittal_Train
|
| 308 |
+
BraTS24_TumorLesionSize_Task07_Sagittal_Test
|
| 309 |
+
BraTS24_TumorLesionSize_Task07_Coronal_Train
|
| 310 |
+
BraTS24_TumorLesionSize_Task07_Coronal_Test
|
| 311 |
+
BraTS24_TumorLesionSize_Task07_Axial_Train
|
| 312 |
+
BraTS24_TumorLesionSize_Task07_Axial_Test
|
| 313 |
+
BraTS24_TumorLesionSize_Task08_Sagittal_Train
|
| 314 |
+
BraTS24_TumorLesionSize_Task08_Sagittal_Test
|
| 315 |
+
BraTS24_TumorLesionSize_Task08_Coronal_Train
|
| 316 |
+
BraTS24_TumorLesionSize_Task08_Coronal_Test
|
| 317 |
+
BraTS24_TumorLesionSize_Task08_Axial_Train
|
| 318 |
+
BraTS24_TumorLesionSize_Task08_Axial_Test
|
| 319 |
+
BraTS24_TumorLesionSize_Task09_Sagittal_Train
|
| 320 |
+
BraTS24_TumorLesionSize_Task09_Sagittal_Test
|
| 321 |
+
BraTS24_TumorLesionSize_Task09_Coronal_Train
|
| 322 |
+
BraTS24_TumorLesionSize_Task09_Coronal_Test
|
| 323 |
+
BraTS24_TumorLesionSize_Task09_Axial_Train
|
| 324 |
+
BraTS24_TumorLesionSize_Task09_Axial_Test
|
| 325 |
+
BraTS24_TumorLesionSize_Task10_Sagittal_Train
|
| 326 |
+
BraTS24_TumorLesionSize_Task10_Sagittal_Test
|
| 327 |
+
BraTS24_TumorLesionSize_Task10_Coronal_Train
|
| 328 |
+
BraTS24_TumorLesionSize_Task10_Coronal_Test
|
| 329 |
+
BraTS24_TumorLesionSize_Task10_Axial_Train
|
| 330 |
+
BraTS24_TumorLesionSize_Task10_Axial_Test
|
| 331 |
+
BraTS24_TumorLesionSize_Task11_Sagittal_Train
|
| 332 |
+
BraTS24_TumorLesionSize_Task11_Sagittal_Test
|
| 333 |
+
BraTS24_TumorLesionSize_Task11_Coronal_Train
|
| 334 |
+
BraTS24_TumorLesionSize_Task11_Coronal_Test
|
| 335 |
+
BraTS24_TumorLesionSize_Task11_Axial_Train
|
| 336 |
+
BraTS24_TumorLesionSize_Task11_Axial_Test
|
| 337 |
+
BraTS24_TumorLesionSize_Task12_Sagittal_Train
|
| 338 |
+
BraTS24_TumorLesionSize_Task12_Sagittal_Test
|
| 339 |
+
BraTS24_TumorLesionSize_Task12_Coronal_Train
|
| 340 |
+
BraTS24_TumorLesionSize_Task12_Coronal_Test
|
| 341 |
+
BraTS24_TumorLesionSize_Task12_Axial_Train
|
| 342 |
+
BraTS24_TumorLesionSize_Task12_Axial_Test
|
| 343 |
+
CAMUS_MaskSize_Task01_Sagittal_Train
|
| 344 |
+
CAMUS_MaskSize_Task01_Sagittal_Test
|
| 345 |
+
CAMUS_MaskSize_Task01_Coronal_Train
|
| 346 |
+
CAMUS_MaskSize_Task01_Coronal_Test
|
| 347 |
+
CAMUS_MaskSize_Task01_Axial_Train
|
| 348 |
+
CAMUS_MaskSize_Task01_Axial_Test
|
| 349 |
+
CAMUS_BoxSize_Task01_Sagittal_Train
|
| 350 |
+
CAMUS_BoxSize_Task01_Sagittal_Test
|
| 351 |
+
CAMUS_BoxSize_Task01_Coronal_Train
|
| 352 |
+
CAMUS_BoxSize_Task01_Coronal_Test
|
| 353 |
+
CAMUS_BoxSize_Task01_Axial_Train
|
| 354 |
+
CAMUS_BoxSize_Task01_Axial_Test
|
| 355 |
+
Ceph-Biometrics-400_BiometricsFromLandmarks_Distance_Task01_Sagittal_Train
|
| 356 |
+
Ceph-Biometrics-400_BiometricsFromLandmarks_Distance_Task01_Sagittal_Test
|
| 357 |
+
Ceph-Biometrics-400_BiometricsFromLandmarks_Angle_Task01_Sagittal_Train
|
| 358 |
+
Ceph-Biometrics-400_BiometricsFromLandmarks_Angle_Task01_Sagittal_Test
|
| 359 |
+
CrossMoDA_MaskSize_Task01_Sagittal_Train
|
| 360 |
+
CrossMoDA_MaskSize_Task01_Sagittal_Test
|
| 361 |
+
CrossMoDA_MaskSize_Task01_Coronal_Train
|
| 362 |
+
CrossMoDA_MaskSize_Task01_Coronal_Test
|
| 363 |
+
CrossMoDA_MaskSize_Task01_Axial_Train
|
| 364 |
+
CrossMoDA_MaskSize_Task01_Axial_Test
|
| 365 |
+
CrossMoDA_BoxSize_Task01_Sagittal_Train
|
| 366 |
+
CrossMoDA_BoxSize_Task01_Sagittal_Test
|
| 367 |
+
CrossMoDA_BoxSize_Task01_Coronal_Train
|
| 368 |
+
CrossMoDA_BoxSize_Task01_Coronal_Test
|
| 369 |
+
CrossMoDA_BoxSize_Task01_Axial_Train
|
| 370 |
+
CrossMoDA_BoxSize_Task01_Axial_Test
|
| 371 |
+
FeTA24_MaskSize_Task01_Sagittal_Train
|
| 372 |
+
FeTA24_MaskSize_Task01_Sagittal_Test
|
| 373 |
+
FeTA24_MaskSize_Task01_Coronal_Train
|
| 374 |
+
FeTA24_MaskSize_Task01_Coronal_Test
|
| 375 |
+
FeTA24_MaskSize_Task01_Axial_Train
|
| 376 |
+
FeTA24_MaskSize_Task01_Axial_Test
|
| 377 |
+
FeTA24_BoxSize_Task01_Sagittal_Train
|
| 378 |
+
FeTA24_BoxSize_Task01_Sagittal_Test
|
| 379 |
+
FeTA24_BoxSize_Task01_Coronal_Train
|
| 380 |
+
FeTA24_BoxSize_Task01_Coronal_Test
|
| 381 |
+
FeTA24_BoxSize_Task01_Axial_Train
|
| 382 |
+
FeTA24_BoxSize_Task01_Axial_Test
|
| 383 |
+
FeTA24_BiometricsFromLandmarks_Task01_Sagittal_Train
|
| 384 |
+
FeTA24_BiometricsFromLandmarks_Task01_Sagittal_Test
|
| 385 |
+
FeTA24_BiometricsFromLandmarks_Task01_Coronal_Train
|
| 386 |
+
FeTA24_BiometricsFromLandmarks_Task01_Coronal_Test
|
| 387 |
+
FeTA24_BiometricsFromLandmarks_Task01_Axial_Train
|
| 388 |
+
FeTA24_BiometricsFromLandmarks_Task01_Axial_Test
|
| 389 |
+
FLARE22_MaskSize_Task01_Sagittal_Train
|
| 390 |
+
FLARE22_MaskSize_Task01_Sagittal_Test
|
| 391 |
+
FLARE22_MaskSize_Task01_Coronal_Train
|
| 392 |
+
FLARE22_MaskSize_Task01_Coronal_Test
|
| 393 |
+
FLARE22_MaskSize_Task01_Axial_Train
|
| 394 |
+
FLARE22_MaskSize_Task01_Axial_Test
|
| 395 |
+
FLARE22_BoxSize_Task01_Sagittal_Train
|
| 396 |
+
FLARE22_BoxSize_Task01_Sagittal_Test
|
| 397 |
+
FLARE22_BoxSize_Task01_Coronal_Train
|
| 398 |
+
FLARE22_BoxSize_Task01_Coronal_Test
|
| 399 |
+
FLARE22_BoxSize_Task01_Axial_Train
|
| 400 |
+
FLARE22_BoxSize_Task01_Axial_Test
|
| 401 |
+
HNTSMRG24_MaskSize_Task01_Sagittal_Train
|
| 402 |
+
HNTSMRG24_MaskSize_Task01_Sagittal_Test
|
| 403 |
+
HNTSMRG24_MaskSize_Task01_Coronal_Train
|
| 404 |
+
HNTSMRG24_MaskSize_Task01_Coronal_Test
|
| 405 |
+
HNTSMRG24_MaskSize_Task01_Axial_Train
|
| 406 |
+
HNTSMRG24_MaskSize_Task01_Axial_Test
|
| 407 |
+
HNTSMRG24_MaskSize_Task02_Sagittal_Train
|
| 408 |
+
HNTSMRG24_MaskSize_Task02_Sagittal_Test
|
| 409 |
+
HNTSMRG24_MaskSize_Task02_Coronal_Train
|
| 410 |
+
HNTSMRG24_MaskSize_Task02_Coronal_Test
|
| 411 |
+
HNTSMRG24_MaskSize_Task02_Axial_Train
|
| 412 |
+
HNTSMRG24_MaskSize_Task02_Axial_Test
|
| 413 |
+
HNTSMRG24_BoxSize_Task01_Sagittal_Train
|
| 414 |
+
HNTSMRG24_BoxSize_Task01_Sagittal_Test
|
| 415 |
+
HNTSMRG24_BoxSize_Task01_Coronal_Train
|
| 416 |
+
HNTSMRG24_BoxSize_Task01_Coronal_Test
|
| 417 |
+
HNTSMRG24_BoxSize_Task01_Axial_Train
|
| 418 |
+
HNTSMRG24_BoxSize_Task01_Axial_Test
|
| 419 |
+
HNTSMRG24_BoxSize_Task02_Sagittal_Train
|
| 420 |
+
HNTSMRG24_BoxSize_Task02_Sagittal_Test
|
| 421 |
+
HNTSMRG24_BoxSize_Task02_Coronal_Train
|
| 422 |
+
HNTSMRG24_BoxSize_Task02_Coronal_Test
|
| 423 |
+
HNTSMRG24_BoxSize_Task02_Axial_Train
|
| 424 |
+
HNTSMRG24_BoxSize_Task02_Axial_Test
|
| 425 |
+
HNTSMRG24_TumorLesionSize_Task01_Sagittal_Train
|
| 426 |
+
HNTSMRG24_TumorLesionSize_Task01_Sagittal_Test
|
| 427 |
+
HNTSMRG24_TumorLesionSize_Task01_Coronal_Train
|
| 428 |
+
HNTSMRG24_TumorLesionSize_Task01_Coronal_Test
|
| 429 |
+
HNTSMRG24_TumorLesionSize_Task01_Axial_Train
|
| 430 |
+
HNTSMRG24_TumorLesionSize_Task01_Axial_Test
|
| 431 |
+
HNTSMRG24_TumorLesionSize_Task02_Sagittal_Train
|
| 432 |
+
HNTSMRG24_TumorLesionSize_Task02_Sagittal_Test
|
| 433 |
+
HNTSMRG24_TumorLesionSize_Task02_Coronal_Train
|
| 434 |
+
HNTSMRG24_TumorLesionSize_Task02_Coronal_Test
|
| 435 |
+
HNTSMRG24_TumorLesionSize_Task02_Axial_Train
|
| 436 |
+
HNTSMRG24_TumorLesionSize_Task02_Axial_Test
|
| 437 |
+
HNTSMRG24_TumorLesionSize_Task03_Sagittal_Train
|
| 438 |
+
HNTSMRG24_TumorLesionSize_Task03_Sagittal_Test
|
| 439 |
+
HNTSMRG24_TumorLesionSize_Task03_Coronal_Train
|
| 440 |
+
HNTSMRG24_TumorLesionSize_Task03_Coronal_Test
|
| 441 |
+
HNTSMRG24_TumorLesionSize_Task03_Axial_Train
|
| 442 |
+
HNTSMRG24_TumorLesionSize_Task03_Axial_Test
|
| 443 |
+
HNTSMRG24_TumorLesionSize_Task04_Sagittal_Train
|
| 444 |
+
HNTSMRG24_TumorLesionSize_Task04_Sagittal_Test
|
| 445 |
+
HNTSMRG24_TumorLesionSize_Task04_Coronal_Train
|
| 446 |
+
HNTSMRG24_TumorLesionSize_Task04_Coronal_Test
|
| 447 |
+
HNTSMRG24_TumorLesionSize_Task04_Axial_Train
|
| 448 |
+
HNTSMRG24_TumorLesionSize_Task04_Axial_Test
|
| 449 |
+
ISLES24_MaskSize_Task01_Sagittal_Train
|
| 450 |
+
ISLES24_MaskSize_Task01_Sagittal_Test
|
| 451 |
+
ISLES24_MaskSize_Task01_Coronal_Train
|
| 452 |
+
ISLES24_MaskSize_Task01_Coronal_Test
|
| 453 |
+
ISLES24_MaskSize_Task01_Axial_Train
|
| 454 |
+
ISLES24_MaskSize_Task01_Axial_Test
|
| 455 |
+
ISLES24_MaskSize_Task02_Sagittal_Train
|
| 456 |
+
ISLES24_MaskSize_Task02_Sagittal_Test
|
| 457 |
+
ISLES24_MaskSize_Task02_Coronal_Train
|
| 458 |
+
ISLES24_MaskSize_Task02_Coronal_Test
|
| 459 |
+
ISLES24_MaskSize_Task02_Axial_Train
|
| 460 |
+
ISLES24_MaskSize_Task02_Axial_Test
|
| 461 |
+
ISLES24_BoxSize_Task01_Sagittal_Train
|
| 462 |
+
ISLES24_BoxSize_Task01_Sagittal_Test
|
| 463 |
+
ISLES24_BoxSize_Task01_Coronal_Train
|
| 464 |
+
ISLES24_BoxSize_Task01_Coronal_Test
|
| 465 |
+
ISLES24_BoxSize_Task01_Axial_Train
|
| 466 |
+
ISLES24_BoxSize_Task01_Axial_Test
|
| 467 |
+
ISLES24_BoxSize_Task02_Sagittal_Train
|
| 468 |
+
ISLES24_BoxSize_Task02_Sagittal_Test
|
| 469 |
+
ISLES24_BoxSize_Task02_Coronal_Train
|
| 470 |
+
ISLES24_BoxSize_Task02_Coronal_Test
|
| 471 |
+
ISLES24_BoxSize_Task02_Axial_Train
|
| 472 |
+
ISLES24_BoxSize_Task02_Axial_Test
|
| 473 |
+
KiPA22_MaskSize_Task01_Sagittal_Train
|
| 474 |
+
KiPA22_MaskSize_Task01_Sagittal_Test
|
| 475 |
+
KiPA22_MaskSize_Task01_Coronal_Train
|
| 476 |
+
KiPA22_MaskSize_Task01_Coronal_Test
|
| 477 |
+
KiPA22_MaskSize_Task01_Axial_Train
|
| 478 |
+
KiPA22_MaskSize_Task01_Axial_Test
|
| 479 |
+
KiPA22_BoxSize_Task01_Sagittal_Train
|
| 480 |
+
KiPA22_BoxSize_Task01_Sagittal_Test
|
| 481 |
+
KiPA22_BoxSize_Task01_Coronal_Train
|
| 482 |
+
KiPA22_BoxSize_Task01_Coronal_Test
|
| 483 |
+
KiPA22_BoxSize_Task01_Axial_Train
|
| 484 |
+
KiPA22_BoxSize_Task01_Axial_Test
|
| 485 |
+
KiPA22_TumorLesionSize_Task01_Sagittal_Train
|
| 486 |
+
KiPA22_TumorLesionSize_Task01_Sagittal_Test
|
| 487 |
+
KiPA22_TumorLesionSize_Task01_Coronal_Train
|
| 488 |
+
KiPA22_TumorLesionSize_Task01_Coronal_Test
|
| 489 |
+
KiPA22_TumorLesionSize_Task01_Axial_Train
|
| 490 |
+
KiPA22_TumorLesionSize_Task01_Axial_Test
|
| 491 |
+
KiTS23_MaskSize_Task01_Sagittal_Train
|
| 492 |
+
KiTS23_MaskSize_Task01_Sagittal_Test
|
| 493 |
+
KiTS23_MaskSize_Task01_Coronal_Train
|
| 494 |
+
KiTS23_MaskSize_Task01_Coronal_Test
|
| 495 |
+
KiTS23_MaskSize_Task01_Axial_Train
|
| 496 |
+
KiTS23_MaskSize_Task01_Axial_Test
|
| 497 |
+
KiTS23_BoxSize_Task01_Sagittal_Train
|
| 498 |
+
KiTS23_BoxSize_Task01_Sagittal_Test
|
| 499 |
+
KiTS23_BoxSize_Task01_Coronal_Train
|
| 500 |
+
KiTS23_BoxSize_Task01_Coronal_Test
|
| 501 |
+
KiTS23_BoxSize_Task01_Axial_Train
|
| 502 |
+
KiTS23_BoxSize_Task01_Axial_Test
|
| 503 |
+
KiTS23_TumorLesionSize_Task01_Sagittal_Train
|
| 504 |
+
KiTS23_TumorLesionSize_Task01_Sagittal_Test
|
| 505 |
+
KiTS23_TumorLesionSize_Task01_Coronal_Train
|
| 506 |
+
KiTS23_TumorLesionSize_Task01_Coronal_Test
|
| 507 |
+
KiTS23_TumorLesionSize_Task01_Axial_Train
|
| 508 |
+
KiTS23_TumorLesionSize_Task01_Axial_Test
|
| 509 |
+
MSD_MaskSize_Task01_Sagittal_Train
|
| 510 |
+
MSD_MaskSize_Task01_Sagittal_Test
|
| 511 |
+
MSD_MaskSize_Task01_Coronal_Train
|
| 512 |
+
MSD_MaskSize_Task01_Coronal_Test
|
| 513 |
+
MSD_MaskSize_Task01_Axial_Train
|
| 514 |
+
MSD_MaskSize_Task01_Axial_Test
|
| 515 |
+
MSD_MaskSize_Task02_Sagittal_Train
|
| 516 |
+
MSD_MaskSize_Task02_Sagittal_Test
|
| 517 |
+
MSD_MaskSize_Task02_Coronal_Train
|
| 518 |
+
MSD_MaskSize_Task02_Coronal_Test
|
| 519 |
+
MSD_MaskSize_Task02_Axial_Train
|
| 520 |
+
MSD_MaskSize_Task02_Axial_Test
|
| 521 |
+
MSD_MaskSize_Task03_Sagittal_Train
|
| 522 |
+
MSD_MaskSize_Task03_Sagittal_Test
|
| 523 |
+
MSD_MaskSize_Task03_Coronal_Train
|
| 524 |
+
MSD_MaskSize_Task03_Coronal_Test
|
| 525 |
+
MSD_MaskSize_Task03_Axial_Train
|
| 526 |
+
MSD_MaskSize_Task03_Axial_Test
|
| 527 |
+
MSD_MaskSize_Task04_Sagittal_Train
|
| 528 |
+
MSD_MaskSize_Task04_Sagittal_Test
|
| 529 |
+
MSD_MaskSize_Task04_Coronal_Train
|
| 530 |
+
MSD_MaskSize_Task04_Coronal_Test
|
| 531 |
+
MSD_MaskSize_Task04_Axial_Train
|
| 532 |
+
MSD_MaskSize_Task04_Axial_Test
|
| 533 |
+
MSD_MaskSize_Task05_Sagittal_Train
|
| 534 |
+
MSD_MaskSize_Task05_Sagittal_Test
|
| 535 |
+
MSD_MaskSize_Task05_Coronal_Train
|
| 536 |
+
MSD_MaskSize_Task05_Coronal_Test
|
| 537 |
+
MSD_MaskSize_Task05_Axial_Train
|
| 538 |
+
MSD_MaskSize_Task05_Axial_Test
|
| 539 |
+
MSD_MaskSize_Task06_Sagittal_Train
|
| 540 |
+
MSD_MaskSize_Task06_Sagittal_Test
|
| 541 |
+
MSD_MaskSize_Task06_Coronal_Train
|
| 542 |
+
MSD_MaskSize_Task06_Coronal_Test
|
| 543 |
+
MSD_MaskSize_Task06_Axial_Train
|
| 544 |
+
MSD_MaskSize_Task06_Axial_Test
|
| 545 |
+
MSD_MaskSize_Task07_Sagittal_Train
|
| 546 |
+
MSD_MaskSize_Task07_Sagittal_Test
|
| 547 |
+
MSD_MaskSize_Task07_Coronal_Train
|
| 548 |
+
MSD_MaskSize_Task07_Coronal_Test
|
| 549 |
+
MSD_MaskSize_Task07_Axial_Train
|
| 550 |
+
MSD_MaskSize_Task07_Axial_Test
|
| 551 |
+
MSD_MaskSize_Task08_Sagittal_Train
|
| 552 |
+
MSD_MaskSize_Task08_Sagittal_Test
|
| 553 |
+
MSD_MaskSize_Task08_Coronal_Train
|
| 554 |
+
MSD_MaskSize_Task08_Coronal_Test
|
| 555 |
+
MSD_MaskSize_Task08_Axial_Train
|
| 556 |
+
MSD_MaskSize_Task08_Axial_Test
|
| 557 |
+
MSD_MaskSize_Task09_Sagittal_Train
|
| 558 |
+
MSD_MaskSize_Task09_Sagittal_Test
|
| 559 |
+
MSD_MaskSize_Task09_Coronal_Train
|
| 560 |
+
MSD_MaskSize_Task09_Coronal_Test
|
| 561 |
+
MSD_MaskSize_Task09_Axial_Train
|
| 562 |
+
MSD_MaskSize_Task09_Axial_Test
|
| 563 |
+
MSD_MaskSize_Task10_Sagittal_Train
|
| 564 |
+
MSD_MaskSize_Task10_Sagittal_Test
|
| 565 |
+
MSD_MaskSize_Task10_Coronal_Train
|
| 566 |
+
MSD_MaskSize_Task10_Coronal_Test
|
| 567 |
+
MSD_MaskSize_Task10_Axial_Train
|
| 568 |
+
MSD_MaskSize_Task10_Axial_Test
|
| 569 |
+
MSD_MaskSize_Task11_Sagittal_Train
|
| 570 |
+
MSD_MaskSize_Task11_Sagittal_Test
|
| 571 |
+
MSD_MaskSize_Task11_Coronal_Train
|
| 572 |
+
MSD_MaskSize_Task11_Coronal_Test
|
| 573 |
+
MSD_MaskSize_Task11_Axial_Train
|
| 574 |
+
MSD_MaskSize_Task11_Axial_Test
|
| 575 |
+
MSD_MaskSize_Task12_Sagittal_Train
|
| 576 |
+
MSD_MaskSize_Task12_Sagittal_Test
|
| 577 |
+
MSD_MaskSize_Task12_Coronal_Train
|
| 578 |
+
MSD_MaskSize_Task12_Coronal_Test
|
| 579 |
+
MSD_MaskSize_Task12_Axial_Train
|
| 580 |
+
MSD_MaskSize_Task12_Axial_Test
|
| 581 |
+
MSD_MaskSize_Task13_Sagittal_Train
|
| 582 |
+
MSD_MaskSize_Task13_Sagittal_Test
|
| 583 |
+
MSD_MaskSize_Task13_Coronal_Train
|
| 584 |
+
MSD_MaskSize_Task13_Coronal_Test
|
| 585 |
+
MSD_MaskSize_Task13_Axial_Train
|
| 586 |
+
MSD_MaskSize_Task13_Axial_Test
|
| 587 |
+
MSD_MaskSize_Task14_Sagittal_Train
|
| 588 |
+
MSD_MaskSize_Task14_Sagittal_Test
|
| 589 |
+
MSD_MaskSize_Task14_Coronal_Train
|
| 590 |
+
MSD_MaskSize_Task14_Coronal_Test
|
| 591 |
+
MSD_MaskSize_Task14_Axial_Train
|
| 592 |
+
MSD_MaskSize_Task14_Axial_Test
|
| 593 |
+
MSD_BoxSize_Task01_Sagittal_Train
|
| 594 |
+
MSD_BoxSize_Task01_Sagittal_Test
|
| 595 |
+
MSD_BoxSize_Task01_Coronal_Train
|
| 596 |
+
MSD_BoxSize_Task01_Coronal_Test
|
| 597 |
+
MSD_BoxSize_Task01_Axial_Train
|
| 598 |
+
MSD_BoxSize_Task01_Axial_Test
|
| 599 |
+
MSD_BoxSize_Task02_Sagittal_Train
|
| 600 |
+
MSD_BoxSize_Task02_Sagittal_Test
|
| 601 |
+
MSD_BoxSize_Task02_Coronal_Train
|
| 602 |
+
MSD_BoxSize_Task02_Coronal_Test
|
| 603 |
+
MSD_BoxSize_Task02_Axial_Train
|
| 604 |
+
MSD_BoxSize_Task02_Axial_Test
|
| 605 |
+
MSD_BoxSize_Task03_Sagittal_Train
|
| 606 |
+
MSD_BoxSize_Task03_Sagittal_Test
|
| 607 |
+
MSD_BoxSize_Task03_Coronal_Train
|
| 608 |
+
MSD_BoxSize_Task03_Coronal_Test
|
| 609 |
+
MSD_BoxSize_Task03_Axial_Train
|
| 610 |
+
MSD_BoxSize_Task03_Axial_Test
|
| 611 |
+
MSD_BoxSize_Task04_Sagittal_Train
|
| 612 |
+
MSD_BoxSize_Task04_Sagittal_Test
|
| 613 |
+
MSD_BoxSize_Task04_Coronal_Train
|
| 614 |
+
MSD_BoxSize_Task04_Coronal_Test
|
| 615 |
+
MSD_BoxSize_Task04_Axial_Train
|
| 616 |
+
MSD_BoxSize_Task04_Axial_Test
|
| 617 |
+
MSD_BoxSize_Task05_Sagittal_Train
|
| 618 |
+
MSD_BoxSize_Task05_Sagittal_Test
|
| 619 |
+
MSD_BoxSize_Task05_Coronal_Train
|
| 620 |
+
MSD_BoxSize_Task05_Coronal_Test
|
| 621 |
+
MSD_BoxSize_Task05_Axial_Train
|
| 622 |
+
MSD_BoxSize_Task05_Axial_Test
|
| 623 |
+
MSD_BoxSize_Task06_Sagittal_Train
|
| 624 |
+
MSD_BoxSize_Task06_Sagittal_Test
|
| 625 |
+
MSD_BoxSize_Task06_Coronal_Train
|
| 626 |
+
MSD_BoxSize_Task06_Coronal_Test
|
| 627 |
+
MSD_BoxSize_Task06_Axial_Train
|
| 628 |
+
MSD_BoxSize_Task06_Axial_Test
|
| 629 |
+
MSD_BoxSize_Task07_Sagittal_Train
|
| 630 |
+
MSD_BoxSize_Task07_Sagittal_Test
|
| 631 |
+
MSD_BoxSize_Task07_Coronal_Train
|
| 632 |
+
MSD_BoxSize_Task07_Coronal_Test
|
| 633 |
+
MSD_BoxSize_Task07_Axial_Train
|
| 634 |
+
MSD_BoxSize_Task07_Axial_Test
|
| 635 |
+
MSD_BoxSize_Task08_Sagittal_Train
|
| 636 |
+
MSD_BoxSize_Task08_Sagittal_Test
|
| 637 |
+
MSD_BoxSize_Task08_Coronal_Train
|
| 638 |
+
MSD_BoxSize_Task08_Coronal_Test
|
| 639 |
+
MSD_BoxSize_Task08_Axial_Train
|
| 640 |
+
MSD_BoxSize_Task08_Axial_Test
|
| 641 |
+
MSD_BoxSize_Task09_Sagittal_Train
|
| 642 |
+
MSD_BoxSize_Task09_Sagittal_Test
|
| 643 |
+
MSD_BoxSize_Task09_Coronal_Train
|
| 644 |
+
MSD_BoxSize_Task09_Coronal_Test
|
| 645 |
+
MSD_BoxSize_Task09_Axial_Train
|
| 646 |
+
MSD_BoxSize_Task09_Axial_Test
|
| 647 |
+
MSD_BoxSize_Task10_Sagittal_Train
|
| 648 |
+
MSD_BoxSize_Task10_Sagittal_Test
|
| 649 |
+
MSD_BoxSize_Task10_Coronal_Train
|
| 650 |
+
MSD_BoxSize_Task10_Coronal_Test
|
| 651 |
+
MSD_BoxSize_Task10_Axial_Train
|
| 652 |
+
MSD_BoxSize_Task10_Axial_Test
|
| 653 |
+
MSD_BoxSize_Task11_Sagittal_Train
|
| 654 |
+
MSD_BoxSize_Task11_Sagittal_Test
|
| 655 |
+
MSD_BoxSize_Task11_Coronal_Train
|
| 656 |
+
MSD_BoxSize_Task11_Coronal_Test
|
| 657 |
+
MSD_BoxSize_Task11_Axial_Train
|
| 658 |
+
MSD_BoxSize_Task11_Axial_Test
|
| 659 |
+
MSD_BoxSize_Task12_Sagittal_Train
|
| 660 |
+
MSD_BoxSize_Task12_Sagittal_Test
|
| 661 |
+
MSD_BoxSize_Task12_Coronal_Train
|
| 662 |
+
MSD_BoxSize_Task12_Coronal_Test
|
| 663 |
+
MSD_BoxSize_Task12_Axial_Train
|
| 664 |
+
MSD_BoxSize_Task12_Axial_Test
|
| 665 |
+
MSD_BoxSize_Task13_Sagittal_Train
|
| 666 |
+
MSD_BoxSize_Task13_Sagittal_Test
|
| 667 |
+
MSD_BoxSize_Task13_Coronal_Train
|
| 668 |
+
MSD_BoxSize_Task13_Coronal_Test
|
| 669 |
+
MSD_BoxSize_Task13_Axial_Train
|
| 670 |
+
MSD_BoxSize_Task13_Axial_Test
|
| 671 |
+
MSD_BoxSize_Task14_Sagittal_Train
|
| 672 |
+
MSD_BoxSize_Task14_Sagittal_Test
|
| 673 |
+
MSD_BoxSize_Task14_Coronal_Train
|
| 674 |
+
MSD_BoxSize_Task14_Coronal_Test
|
| 675 |
+
MSD_BoxSize_Task14_Axial_Train
|
| 676 |
+
MSD_BoxSize_Task14_Axial_Test
|
| 677 |
+
MSD_TumorLesionSize_Task01_Sagittal_Train
|
| 678 |
+
MSD_TumorLesionSize_Task01_Sagittal_Test
|
| 679 |
+
MSD_TumorLesionSize_Task01_Coronal_Train
|
| 680 |
+
MSD_TumorLesionSize_Task01_Coronal_Test
|
| 681 |
+
MSD_TumorLesionSize_Task01_Axial_Train
|
| 682 |
+
MSD_TumorLesionSize_Task01_Axial_Test
|
| 683 |
+
MSD_TumorLesionSize_Task02_Sagittal_Train
|
| 684 |
+
MSD_TumorLesionSize_Task02_Sagittal_Test
|
| 685 |
+
MSD_TumorLesionSize_Task02_Coronal_Train
|
| 686 |
+
MSD_TumorLesionSize_Task02_Coronal_Test
|
| 687 |
+
MSD_TumorLesionSize_Task02_Axial_Train
|
| 688 |
+
MSD_TumorLesionSize_Task02_Axial_Test
|
| 689 |
+
MSD_TumorLesionSize_Task03_Sagittal_Train
|
| 690 |
+
MSD_TumorLesionSize_Task03_Sagittal_Test
|
| 691 |
+
MSD_TumorLesionSize_Task03_Coronal_Train
|
| 692 |
+
MSD_TumorLesionSize_Task03_Coronal_Test
|
| 693 |
+
MSD_TumorLesionSize_Task03_Axial_Train
|
| 694 |
+
MSD_TumorLesionSize_Task03_Axial_Test
|
| 695 |
+
MSD_TumorLesionSize_Task04_Sagittal_Train
|
| 696 |
+
MSD_TumorLesionSize_Task04_Sagittal_Test
|
| 697 |
+
MSD_TumorLesionSize_Task04_Coronal_Train
|
| 698 |
+
MSD_TumorLesionSize_Task04_Coronal_Test
|
| 699 |
+
MSD_TumorLesionSize_Task04_Axial_Train
|
| 700 |
+
MSD_TumorLesionSize_Task04_Axial_Test
|
| 701 |
+
MSD_TumorLesionSize_Task05_Sagittal_Train
|
| 702 |
+
MSD_TumorLesionSize_Task05_Sagittal_Test
|
| 703 |
+
MSD_TumorLesionSize_Task05_Coronal_Train
|
| 704 |
+
MSD_TumorLesionSize_Task05_Coronal_Test
|
| 705 |
+
MSD_TumorLesionSize_Task05_Axial_Train
|
| 706 |
+
MSD_TumorLesionSize_Task05_Axial_Test
|
| 707 |
+
MSD_TumorLesionSize_Task06_Sagittal_Train
|
| 708 |
+
MSD_TumorLesionSize_Task06_Sagittal_Test
|
| 709 |
+
MSD_TumorLesionSize_Task06_Coronal_Train
|
| 710 |
+
MSD_TumorLesionSize_Task06_Coronal_Test
|
| 711 |
+
MSD_TumorLesionSize_Task06_Axial_Train
|
| 712 |
+
MSD_TumorLesionSize_Task06_Axial_Test
|
| 713 |
+
MSD_TumorLesionSize_Task07_Sagittal_Train
|
| 714 |
+
MSD_TumorLesionSize_Task07_Sagittal_Test
|
| 715 |
+
MSD_TumorLesionSize_Task07_Coronal_Train
|
| 716 |
+
MSD_TumorLesionSize_Task07_Coronal_Test
|
| 717 |
+
MSD_TumorLesionSize_Task07_Axial_Train
|
| 718 |
+
MSD_TumorLesionSize_Task07_Axial_Test
|
| 719 |
+
MSD_TumorLesionSize_Task08_Sagittal_Train
|
| 720 |
+
MSD_TumorLesionSize_Task08_Sagittal_Test
|
| 721 |
+
MSD_TumorLesionSize_Task08_Coronal_Train
|
| 722 |
+
MSD_TumorLesionSize_Task08_Coronal_Test
|
| 723 |
+
MSD_TumorLesionSize_Task08_Axial_Train
|
| 724 |
+
MSD_TumorLesionSize_Task08_Axial_Test
|
| 725 |
+
OAIZIB-CM_MaskSize_Task01_Sagittal_Train
|
| 726 |
+
OAIZIB-CM_MaskSize_Task01_Sagittal_Test
|
| 727 |
+
OAIZIB-CM_MaskSize_Task01_Coronal_Train
|
| 728 |
+
OAIZIB-CM_MaskSize_Task01_Coronal_Test
|
| 729 |
+
OAIZIB-CM_MaskSize_Task01_Axial_Train
|
| 730 |
+
OAIZIB-CM_MaskSize_Task01_Axial_Test
|
| 731 |
+
OAIZIB-CM_BoxSize_Task01_Sagittal_Train
|
| 732 |
+
OAIZIB-CM_BoxSize_Task01_Sagittal_Test
|
| 733 |
+
OAIZIB-CM_BoxSize_Task01_Coronal_Train
|
| 734 |
+
OAIZIB-CM_BoxSize_Task01_Coronal_Test
|
| 735 |
+
OAIZIB-CM_BoxSize_Task01_Axial_Train
|
| 736 |
+
OAIZIB-CM_BoxSize_Task01_Axial_Test
|
| 737 |
+
SKM-TEA_MaskSize_Task01_Sagittal_Train
|
| 738 |
+
SKM-TEA_MaskSize_Task01_Sagittal_Test
|
| 739 |
+
SKM-TEA_MaskSize_Task01_Coronal_Train
|
| 740 |
+
SKM-TEA_MaskSize_Task01_Coronal_Test
|
| 741 |
+
SKM-TEA_MaskSize_Task01_Axial_Train
|
| 742 |
+
SKM-TEA_MaskSize_Task01_Axial_Test
|
| 743 |
+
SKM-TEA_MaskSize_Task02_Sagittal_Train
|
| 744 |
+
SKM-TEA_MaskSize_Task02_Sagittal_Test
|
| 745 |
+
SKM-TEA_MaskSize_Task02_Coronal_Train
|
| 746 |
+
SKM-TEA_MaskSize_Task02_Coronal_Test
|
| 747 |
+
SKM-TEA_MaskSize_Task02_Axial_Train
|
| 748 |
+
SKM-TEA_MaskSize_Task02_Axial_Test
|
| 749 |
+
SKM-TEA_BoxSize_Task01_Sagittal_Train
|
| 750 |
+
SKM-TEA_BoxSize_Task01_Sagittal_Test
|
| 751 |
+
SKM-TEA_BoxSize_Task01_Coronal_Train
|
| 752 |
+
SKM-TEA_BoxSize_Task01_Coronal_Test
|
| 753 |
+
SKM-TEA_BoxSize_Task01_Axial_Train
|
| 754 |
+
SKM-TEA_BoxSize_Task01_Axial_Test
|
| 755 |
+
SKM-TEA_BoxSize_Task02_Sagittal_Train
|
| 756 |
+
SKM-TEA_BoxSize_Task02_Sagittal_Test
|
| 757 |
+
SKM-TEA_BoxSize_Task02_Coronal_Train
|
| 758 |
+
SKM-TEA_BoxSize_Task02_Coronal_Test
|
| 759 |
+
SKM-TEA_BoxSize_Task02_Axial_Train
|
| 760 |
+
SKM-TEA_BoxSize_Task02_Axial_Test
|
| 761 |
+
ToothFairy2_MaskSize_Task01_Sagittal_Train
|
| 762 |
+
ToothFairy2_MaskSize_Task01_Sagittal_Test
|
| 763 |
+
ToothFairy2_MaskSize_Task01_Coronal_Train
|
| 764 |
+
ToothFairy2_MaskSize_Task01_Coronal_Test
|
| 765 |
+
ToothFairy2_MaskSize_Task01_Axial_Train
|
| 766 |
+
ToothFairy2_MaskSize_Task01_Axial_Test
|
| 767 |
+
ToothFairy2_BoxSize_Task01_Sagittal_Train
|
| 768 |
+
ToothFairy2_BoxSize_Task01_Sagittal_Test
|
| 769 |
+
ToothFairy2_BoxSize_Task01_Coronal_Train
|
| 770 |
+
ToothFairy2_BoxSize_Task01_Coronal_Test
|
| 771 |
+
ToothFairy2_BoxSize_Task01_Axial_Train
|
| 772 |
+
ToothFairy2_BoxSize_Task01_Axial_Test
|
| 773 |
+
TopCoW24_MaskSize_Task01_Sagittal_Train
|
| 774 |
+
TopCoW24_MaskSize_Task01_Sagittal_Test
|
| 775 |
+
TopCoW24_MaskSize_Task01_Coronal_Train
|
| 776 |
+
TopCoW24_MaskSize_Task01_Coronal_Test
|
| 777 |
+
TopCoW24_MaskSize_Task01_Axial_Train
|
| 778 |
+
TopCoW24_MaskSize_Task01_Axial_Test
|
| 779 |
+
TopCoW24_MaskSize_Task02_Sagittal_Train
|
| 780 |
+
TopCoW24_MaskSize_Task02_Sagittal_Test
|
| 781 |
+
TopCoW24_MaskSize_Task02_Coronal_Train
|
| 782 |
+
TopCoW24_MaskSize_Task02_Coronal_Test
|
| 783 |
+
TopCoW24_MaskSize_Task02_Axial_Train
|
| 784 |
+
TopCoW24_MaskSize_Task02_Axial_Test
|
| 785 |
+
TopCoW24_BoxSize_Task01_Sagittal_Train
|
| 786 |
+
TopCoW24_BoxSize_Task01_Sagittal_Test
|
| 787 |
+
TopCoW24_BoxSize_Task01_Coronal_Train
|
| 788 |
+
TopCoW24_BoxSize_Task01_Coronal_Test
|
| 789 |
+
TopCoW24_BoxSize_Task01_Axial_Train
|
| 790 |
+
TopCoW24_BoxSize_Task01_Axial_Test
|
| 791 |
+
TopCoW24_BoxSize_Task02_Sagittal_Train
|
| 792 |
+
TopCoW24_BoxSize_Task02_Sagittal_Test
|
| 793 |
+
TopCoW24_BoxSize_Task02_Coronal_Train
|
| 794 |
+
TopCoW24_BoxSize_Task02_Coronal_Test
|
| 795 |
+
TopCoW24_BoxSize_Task02_Axial_Train
|
| 796 |
+
TopCoW24_BoxSize_Task02_Axial_Test
|
| 797 |
+
TotalSegmentator_MaskSize_Task01_Sagittal_Train
|
| 798 |
+
TotalSegmentator_MaskSize_Task01_Sagittal_Test
|
| 799 |
+
TotalSegmentator_MaskSize_Task01_Coronal_Train
|
| 800 |
+
TotalSegmentator_MaskSize_Task01_Coronal_Test
|
| 801 |
+
TotalSegmentator_MaskSize_Task01_Axial_Train
|
| 802 |
+
TotalSegmentator_MaskSize_Task01_Axial_Test
|
| 803 |
+
TotalSegmentator_MaskSize_Task02_Sagittal_Train
|
| 804 |
+
TotalSegmentator_MaskSize_Task02_Sagittal_Test
|
| 805 |
+
TotalSegmentator_MaskSize_Task02_Coronal_Train
|
| 806 |
+
TotalSegmentator_MaskSize_Task02_Coronal_Test
|
| 807 |
+
TotalSegmentator_MaskSize_Task02_Axial_Train
|
| 808 |
+
TotalSegmentator_MaskSize_Task02_Axial_Test
|
| 809 |
+
TotalSegmentator_BoxSize_Task01_Sagittal_Train
|
| 810 |
+
TotalSegmentator_BoxSize_Task01_Sagittal_Test
|
| 811 |
+
TotalSegmentator_BoxSize_Task01_Coronal_Train
|
| 812 |
+
TotalSegmentator_BoxSize_Task01_Coronal_Test
|
| 813 |
+
TotalSegmentator_BoxSize_Task01_Axial_Train
|
| 814 |
+
TotalSegmentator_BoxSize_Task01_Axial_Test
|
| 815 |
+
TotalSegmentator_BoxSize_Task02_Sagittal_Train
|
| 816 |
+
TotalSegmentator_BoxSize_Task02_Sagittal_Test
|
| 817 |
+
TotalSegmentator_BoxSize_Task02_Coronal_Train
|
| 818 |
+
TotalSegmentator_BoxSize_Task02_Coronal_Test
|
| 819 |
+
TotalSegmentator_BoxSize_Task02_Axial_Train
|
| 820 |
+
TotalSegmentator_BoxSize_Task02_Axial_Test
|
info/ConfigurationsList_Test.csv
ADDED
|
@@ -0,0 +1,410 @@
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
| 1 |
+
AbdomenAtlas1.0Mini_MaskSize_Task01_Sagittal_Test
|
| 2 |
+
AbdomenAtlas1.0Mini_MaskSize_Task01_Coronal_Test
|
| 3 |
+
AbdomenAtlas1.0Mini_MaskSize_Task01_Axial_Test
|
| 4 |
+
AbdomenAtlas1.0Mini_BoxSize_Task01_Sagittal_Test
|
| 5 |
+
AbdomenAtlas1.0Mini_BoxSize_Task01_Coronal_Test
|
| 6 |
+
AbdomenAtlas1.0Mini_BoxSize_Task01_Axial_Test
|
| 7 |
+
AbdomenCT-1K_MaskSize_Task01_Sagittal_Test
|
| 8 |
+
AbdomenCT-1K_MaskSize_Task01_Coronal_Test
|
| 9 |
+
AbdomenCT-1K_MaskSize_Task01_Axial_Test
|
| 10 |
+
AbdomenCT-1K_BoxSize_Task01_Sagittal_Test
|
| 11 |
+
AbdomenCT-1K_BoxSize_Task01_Coronal_Test
|
| 12 |
+
AbdomenCT-1K_BoxSize_Task01_Axial_Test
|
| 13 |
+
ACDC_MaskSize_Task01_Sagittal_Test
|
| 14 |
+
ACDC_MaskSize_Task01_Coronal_Test
|
| 15 |
+
ACDC_MaskSize_Task01_Axial_Test
|
| 16 |
+
ACDC_BoxSize_Task01_Sagittal_Test
|
| 17 |
+
ACDC_BoxSize_Task01_Coronal_Test
|
| 18 |
+
ACDC_BoxSize_Task01_Axial_Test
|
| 19 |
+
AMOS22_MaskSize_Task01_Sagittal_Test
|
| 20 |
+
AMOS22_MaskSize_Task01_Coronal_Test
|
| 21 |
+
AMOS22_MaskSize_Task01_Axial_Test
|
| 22 |
+
AMOS22_MaskSize_Task02_Sagittal_Test
|
| 23 |
+
AMOS22_MaskSize_Task02_Coronal_Test
|
| 24 |
+
AMOS22_MaskSize_Task02_Axial_Test
|
| 25 |
+
AMOS22_BoxSize_Task01_Sagittal_Test
|
| 26 |
+
AMOS22_BoxSize_Task01_Coronal_Test
|
| 27 |
+
AMOS22_BoxSize_Task01_Axial_Test
|
| 28 |
+
AMOS22_BoxSize_Task02_Sagittal_Test
|
| 29 |
+
AMOS22_BoxSize_Task02_Coronal_Test
|
| 30 |
+
AMOS22_BoxSize_Task02_Axial_Test
|
| 31 |
+
autoPET-III_MaskSize_Task01_Sagittal_Test
|
| 32 |
+
autoPET-III_MaskSize_Task01_Coronal_Test
|
| 33 |
+
autoPET-III_MaskSize_Task01_Axial_Test
|
| 34 |
+
autoPET-III_MaskSize_Task02_Sagittal_Test
|
| 35 |
+
autoPET-III_MaskSize_Task02_Coronal_Test
|
| 36 |
+
autoPET-III_MaskSize_Task02_Axial_Test
|
| 37 |
+
autoPET-III_BoxSize_Task01_Sagittal_Test
|
| 38 |
+
autoPET-III_BoxSize_Task01_Coronal_Test
|
| 39 |
+
autoPET-III_BoxSize_Task01_Axial_Test
|
| 40 |
+
autoPET-III_BoxSize_Task02_Sagittal_Test
|
| 41 |
+
autoPET-III_BoxSize_Task02_Coronal_Test
|
| 42 |
+
autoPET-III_BoxSize_Task02_Axial_Test
|
| 43 |
+
autoPET-III_TumorLesionSize_Task01_Sagittal_Test
|
| 44 |
+
autoPET-III_TumorLesionSize_Task01_Coronal_Test
|
| 45 |
+
autoPET-III_TumorLesionSize_Task01_Axial_Test
|
| 46 |
+
BCV15_MaskSize_Task01_Sagittal_Test
|
| 47 |
+
BCV15_MaskSize_Task01_Coronal_Test
|
| 48 |
+
BCV15_MaskSize_Task01_Axial_Test
|
| 49 |
+
BCV15_MaskSize_Task02_Sagittal_Test
|
| 50 |
+
BCV15_MaskSize_Task02_Coronal_Test
|
| 51 |
+
BCV15_MaskSize_Task02_Axial_Test
|
| 52 |
+
BCV15_BoxSize_Task01_Sagittal_Test
|
| 53 |
+
BCV15_BoxSize_Task01_Coronal_Test
|
| 54 |
+
BCV15_BoxSize_Task01_Axial_Test
|
| 55 |
+
BCV15_BoxSize_Task02_Sagittal_Test
|
| 56 |
+
BCV15_BoxSize_Task02_Coronal_Test
|
| 57 |
+
BCV15_BoxSize_Task02_Axial_Test
|
| 58 |
+
BraTS24_MaskSize_Task01_Sagittal_Test
|
| 59 |
+
BraTS24_MaskSize_Task01_Coronal_Test
|
| 60 |
+
BraTS24_MaskSize_Task01_Axial_Test
|
| 61 |
+
BraTS24_MaskSize_Task02_Sagittal_Test
|
| 62 |
+
BraTS24_MaskSize_Task02_Coronal_Test
|
| 63 |
+
BraTS24_MaskSize_Task02_Axial_Test
|
| 64 |
+
BraTS24_MaskSize_Task03_Sagittal_Test
|
| 65 |
+
BraTS24_MaskSize_Task03_Coronal_Test
|
| 66 |
+
BraTS24_MaskSize_Task03_Axial_Test
|
| 67 |
+
BraTS24_MaskSize_Task04_Sagittal_Test
|
| 68 |
+
BraTS24_MaskSize_Task04_Coronal_Test
|
| 69 |
+
BraTS24_MaskSize_Task04_Axial_Test
|
| 70 |
+
BraTS24_MaskSize_Task05_Sagittal_Test
|
| 71 |
+
BraTS24_MaskSize_Task05_Coronal_Test
|
| 72 |
+
BraTS24_MaskSize_Task05_Axial_Test
|
| 73 |
+
BraTS24_MaskSize_Task06_Sagittal_Test
|
| 74 |
+
BraTS24_MaskSize_Task06_Coronal_Test
|
| 75 |
+
BraTS24_MaskSize_Task06_Axial_Test
|
| 76 |
+
BraTS24_MaskSize_Task07_Sagittal_Test
|
| 77 |
+
BraTS24_MaskSize_Task07_Coronal_Test
|
| 78 |
+
BraTS24_MaskSize_Task07_Axial_Test
|
| 79 |
+
BraTS24_MaskSize_Task08_Sagittal_Test
|
| 80 |
+
BraTS24_MaskSize_Task08_Coronal_Test
|
| 81 |
+
BraTS24_MaskSize_Task08_Axial_Test
|
| 82 |
+
BraTS24_MaskSize_Task09_Sagittal_Test
|
| 83 |
+
BraTS24_MaskSize_Task09_Coronal_Test
|
| 84 |
+
BraTS24_MaskSize_Task09_Axial_Test
|
| 85 |
+
BraTS24_MaskSize_Task10_Sagittal_Test
|
| 86 |
+
BraTS24_MaskSize_Task10_Coronal_Test
|
| 87 |
+
BraTS24_MaskSize_Task10_Axial_Test
|
| 88 |
+
BraTS24_MaskSize_Task11_Sagittal_Test
|
| 89 |
+
BraTS24_MaskSize_Task11_Coronal_Test
|
| 90 |
+
BraTS24_MaskSize_Task11_Axial_Test
|
| 91 |
+
BraTS24_MaskSize_Task12_Sagittal_Test
|
| 92 |
+
BraTS24_MaskSize_Task12_Coronal_Test
|
| 93 |
+
BraTS24_MaskSize_Task12_Axial_Test
|
| 94 |
+
BraTS24_MaskSize_Task13_Sagittal_Test
|
| 95 |
+
BraTS24_MaskSize_Task13_Coronal_Test
|
| 96 |
+
BraTS24_MaskSize_Task13_Axial_Test
|
| 97 |
+
BraTS24_BoxSize_Task01_Sagittal_Test
|
| 98 |
+
BraTS24_BoxSize_Task01_Coronal_Test
|
| 99 |
+
BraTS24_BoxSize_Task01_Axial_Test
|
| 100 |
+
BraTS24_BoxSize_Task02_Sagittal_Test
|
| 101 |
+
BraTS24_BoxSize_Task02_Coronal_Test
|
| 102 |
+
BraTS24_BoxSize_Task02_Axial_Test
|
| 103 |
+
BraTS24_BoxSize_Task03_Sagittal_Test
|
| 104 |
+
BraTS24_BoxSize_Task03_Coronal_Test
|
| 105 |
+
BraTS24_BoxSize_Task03_Axial_Test
|
| 106 |
+
BraTS24_BoxSize_Task04_Sagittal_Test
|
| 107 |
+
BraTS24_BoxSize_Task04_Coronal_Test
|
| 108 |
+
BraTS24_BoxSize_Task04_Axial_Test
|
| 109 |
+
BraTS24_BoxSize_Task05_Sagittal_Test
|
| 110 |
+
BraTS24_BoxSize_Task05_Coronal_Test
|
| 111 |
+
BraTS24_BoxSize_Task05_Axial_Test
|
| 112 |
+
BraTS24_BoxSize_Task06_Sagittal_Test
|
| 113 |
+
BraTS24_BoxSize_Task06_Coronal_Test
|
| 114 |
+
BraTS24_BoxSize_Task06_Axial_Test
|
| 115 |
+
BraTS24_BoxSize_Task07_Sagittal_Test
|
| 116 |
+
BraTS24_BoxSize_Task07_Coronal_Test
|
| 117 |
+
BraTS24_BoxSize_Task07_Axial_Test
|
| 118 |
+
BraTS24_BoxSize_Task08_Sagittal_Test
|
| 119 |
+
BraTS24_BoxSize_Task08_Coronal_Test
|
| 120 |
+
BraTS24_BoxSize_Task08_Axial_Test
|
| 121 |
+
BraTS24_BoxSize_Task09_Sagittal_Test
|
| 122 |
+
BraTS24_BoxSize_Task09_Coronal_Test
|
| 123 |
+
BraTS24_BoxSize_Task09_Axial_Test
|
| 124 |
+
BraTS24_BoxSize_Task10_Sagittal_Test
|
| 125 |
+
BraTS24_BoxSize_Task10_Coronal_Test
|
| 126 |
+
BraTS24_BoxSize_Task10_Axial_Test
|
| 127 |
+
BraTS24_BoxSize_Task11_Sagittal_Test
|
| 128 |
+
BraTS24_BoxSize_Task11_Coronal_Test
|
| 129 |
+
BraTS24_BoxSize_Task11_Axial_Test
|
| 130 |
+
BraTS24_BoxSize_Task12_Sagittal_Test
|
| 131 |
+
BraTS24_BoxSize_Task12_Coronal_Test
|
| 132 |
+
BraTS24_BoxSize_Task12_Axial_Test
|
| 133 |
+
BraTS24_BoxSize_Task13_Sagittal_Test
|
| 134 |
+
BraTS24_BoxSize_Task13_Coronal_Test
|
| 135 |
+
BraTS24_BoxSize_Task13_Axial_Test
|
| 136 |
+
BraTS24_TumorLesionSize_Task01_Sagittal_Test
|
| 137 |
+
BraTS24_TumorLesionSize_Task01_Coronal_Test
|
| 138 |
+
BraTS24_TumorLesionSize_Task01_Axial_Test
|
| 139 |
+
BraTS24_TumorLesionSize_Task02_Sagittal_Test
|
| 140 |
+
BraTS24_TumorLesionSize_Task02_Coronal_Test
|
| 141 |
+
BraTS24_TumorLesionSize_Task02_Axial_Test
|
| 142 |
+
BraTS24_TumorLesionSize_Task03_Sagittal_Test
|
| 143 |
+
BraTS24_TumorLesionSize_Task03_Coronal_Test
|
| 144 |
+
BraTS24_TumorLesionSize_Task03_Axial_Test
|
| 145 |
+
BraTS24_TumorLesionSize_Task04_Sagittal_Test
|
| 146 |
+
BraTS24_TumorLesionSize_Task04_Coronal_Test
|
| 147 |
+
BraTS24_TumorLesionSize_Task04_Axial_Test
|
| 148 |
+
BraTS24_TumorLesionSize_Task05_Sagittal_Test
|
| 149 |
+
BraTS24_TumorLesionSize_Task05_Coronal_Test
|
| 150 |
+
BraTS24_TumorLesionSize_Task05_Axial_Test
|
| 151 |
+
BraTS24_TumorLesionSize_Task06_Sagittal_Test
|
| 152 |
+
BraTS24_TumorLesionSize_Task06_Coronal_Test
|
| 153 |
+
BraTS24_TumorLesionSize_Task06_Axial_Test
|
| 154 |
+
BraTS24_TumorLesionSize_Task07_Sagittal_Test
|
| 155 |
+
BraTS24_TumorLesionSize_Task07_Coronal_Test
|
| 156 |
+
BraTS24_TumorLesionSize_Task07_Axial_Test
|
| 157 |
+
BraTS24_TumorLesionSize_Task08_Sagittal_Test
|
| 158 |
+
BraTS24_TumorLesionSize_Task08_Coronal_Test
|
| 159 |
+
BraTS24_TumorLesionSize_Task08_Axial_Test
|
| 160 |
+
BraTS24_TumorLesionSize_Task09_Sagittal_Test
|
| 161 |
+
BraTS24_TumorLesionSize_Task09_Coronal_Test
|
| 162 |
+
BraTS24_TumorLesionSize_Task09_Axial_Test
|
| 163 |
+
BraTS24_TumorLesionSize_Task10_Sagittal_Test
|
| 164 |
+
BraTS24_TumorLesionSize_Task10_Coronal_Test
|
| 165 |
+
BraTS24_TumorLesionSize_Task10_Axial_Test
|
| 166 |
+
BraTS24_TumorLesionSize_Task11_Sagittal_Test
|
| 167 |
+
BraTS24_TumorLesionSize_Task11_Coronal_Test
|
| 168 |
+
BraTS24_TumorLesionSize_Task11_Axial_Test
|
| 169 |
+
BraTS24_TumorLesionSize_Task12_Sagittal_Test
|
| 170 |
+
BraTS24_TumorLesionSize_Task12_Coronal_Test
|
| 171 |
+
BraTS24_TumorLesionSize_Task12_Axial_Test
|
| 172 |
+
CAMUS_MaskSize_Task01_Sagittal_Test
|
| 173 |
+
CAMUS_MaskSize_Task01_Coronal_Test
|
| 174 |
+
CAMUS_MaskSize_Task01_Axial_Test
|
| 175 |
+
CAMUS_BoxSize_Task01_Sagittal_Test
|
| 176 |
+
CAMUS_BoxSize_Task01_Coronal_Test
|
| 177 |
+
CAMUS_BoxSize_Task01_Axial_Test
|
| 178 |
+
Ceph-Biometrics-400_BiometricsFromLandmarks_Distance_Task01_Sagittal_Test
|
| 179 |
+
Ceph-Biometrics-400_BiometricsFromLandmarks_Angle_Task01_Sagittal_Test
|
| 180 |
+
CrossMoDA_MaskSize_Task01_Sagittal_Test
|
| 181 |
+
CrossMoDA_MaskSize_Task01_Coronal_Test
|
| 182 |
+
CrossMoDA_MaskSize_Task01_Axial_Test
|
| 183 |
+
CrossMoDA_BoxSize_Task01_Sagittal_Test
|
| 184 |
+
CrossMoDA_BoxSize_Task01_Coronal_Test
|
| 185 |
+
CrossMoDA_BoxSize_Task01_Axial_Test
|
| 186 |
+
FeTA24_MaskSize_Task01_Sagittal_Test
|
| 187 |
+
FeTA24_MaskSize_Task01_Coronal_Test
|
| 188 |
+
FeTA24_MaskSize_Task01_Axial_Test
|
| 189 |
+
FeTA24_BoxSize_Task01_Sagittal_Test
|
| 190 |
+
FeTA24_BoxSize_Task01_Coronal_Test
|
| 191 |
+
FeTA24_BoxSize_Task01_Axial_Test
|
| 192 |
+
FeTA24_BiometricsFromLandmarks_Task01_Sagittal_Test
|
| 193 |
+
FeTA24_BiometricsFromLandmarks_Task01_Coronal_Test
|
| 194 |
+
FeTA24_BiometricsFromLandmarks_Task01_Axial_Test
|
| 195 |
+
FLARE22_MaskSize_Task01_Sagittal_Test
|
| 196 |
+
FLARE22_MaskSize_Task01_Coronal_Test
|
| 197 |
+
FLARE22_MaskSize_Task01_Axial_Test
|
| 198 |
+
FLARE22_BoxSize_Task01_Sagittal_Test
|
| 199 |
+
FLARE22_BoxSize_Task01_Coronal_Test
|
| 200 |
+
FLARE22_BoxSize_Task01_Axial_Test
|
| 201 |
+
HNTSMRG24_MaskSize_Task01_Sagittal_Test
|
| 202 |
+
HNTSMRG24_MaskSize_Task01_Coronal_Test
|
| 203 |
+
HNTSMRG24_MaskSize_Task01_Axial_Test
|
| 204 |
+
HNTSMRG24_MaskSize_Task02_Sagittal_Test
|
| 205 |
+
HNTSMRG24_MaskSize_Task02_Coronal_Test
|
| 206 |
+
HNTSMRG24_MaskSize_Task02_Axial_Test
|
| 207 |
+
HNTSMRG24_BoxSize_Task01_Sagittal_Test
|
| 208 |
+
HNTSMRG24_BoxSize_Task01_Coronal_Test
|
| 209 |
+
HNTSMRG24_BoxSize_Task01_Axial_Test
|
| 210 |
+
HNTSMRG24_BoxSize_Task02_Sagittal_Test
|
| 211 |
+
HNTSMRG24_BoxSize_Task02_Coronal_Test
|
| 212 |
+
HNTSMRG24_BoxSize_Task02_Axial_Test
|
| 213 |
+
HNTSMRG24_TumorLesionSize_Task01_Sagittal_Test
|
| 214 |
+
HNTSMRG24_TumorLesionSize_Task01_Coronal_Test
|
| 215 |
+
HNTSMRG24_TumorLesionSize_Task01_Axial_Test
|
| 216 |
+
HNTSMRG24_TumorLesionSize_Task02_Sagittal_Test
|
| 217 |
+
HNTSMRG24_TumorLesionSize_Task02_Coronal_Test
|
| 218 |
+
HNTSMRG24_TumorLesionSize_Task02_Axial_Test
|
| 219 |
+
HNTSMRG24_TumorLesionSize_Task03_Sagittal_Test
|
| 220 |
+
HNTSMRG24_TumorLesionSize_Task03_Coronal_Test
|
| 221 |
+
HNTSMRG24_TumorLesionSize_Task03_Axial_Test
|
| 222 |
+
HNTSMRG24_TumorLesionSize_Task04_Sagittal_Test
|
| 223 |
+
HNTSMRG24_TumorLesionSize_Task04_Coronal_Test
|
| 224 |
+
HNTSMRG24_TumorLesionSize_Task04_Axial_Test
|
| 225 |
+
ISLES24_MaskSize_Task01_Sagittal_Test
|
| 226 |
+
ISLES24_MaskSize_Task01_Coronal_Test
|
| 227 |
+
ISLES24_MaskSize_Task01_Axial_Test
|
| 228 |
+
ISLES24_MaskSize_Task02_Sagittal_Test
|
| 229 |
+
ISLES24_MaskSize_Task02_Coronal_Test
|
| 230 |
+
ISLES24_MaskSize_Task02_Axial_Test
|
| 231 |
+
ISLES24_BoxSize_Task01_Sagittal_Test
|
| 232 |
+
ISLES24_BoxSize_Task01_Coronal_Test
|
| 233 |
+
ISLES24_BoxSize_Task01_Axial_Test
|
| 234 |
+
ISLES24_BoxSize_Task02_Sagittal_Test
|
| 235 |
+
ISLES24_BoxSize_Task02_Coronal_Test
|
| 236 |
+
ISLES24_BoxSize_Task02_Axial_Test
|
| 237 |
+
KiPA22_MaskSize_Task01_Sagittal_Test
|
| 238 |
+
KiPA22_MaskSize_Task01_Coronal_Test
|
| 239 |
+
KiPA22_MaskSize_Task01_Axial_Test
|
| 240 |
+
KiPA22_BoxSize_Task01_Sagittal_Test
|
| 241 |
+
KiPA22_BoxSize_Task01_Coronal_Test
|
| 242 |
+
KiPA22_BoxSize_Task01_Axial_Test
|
| 243 |
+
KiPA22_TumorLesionSize_Task01_Sagittal_Test
|
| 244 |
+
KiPA22_TumorLesionSize_Task01_Coronal_Test
|
| 245 |
+
KiPA22_TumorLesionSize_Task01_Axial_Test
|
| 246 |
+
KiTS23_MaskSize_Task01_Sagittal_Test
|
| 247 |
+
KiTS23_MaskSize_Task01_Coronal_Test
|
| 248 |
+
KiTS23_MaskSize_Task01_Axial_Test
|
| 249 |
+
KiTS23_BoxSize_Task01_Sagittal_Test
|
| 250 |
+
KiTS23_BoxSize_Task01_Coronal_Test
|
| 251 |
+
KiTS23_BoxSize_Task01_Axial_Test
|
| 252 |
+
KiTS23_TumorLesionSize_Task01_Sagittal_Test
|
| 253 |
+
KiTS23_TumorLesionSize_Task01_Coronal_Test
|
| 254 |
+
KiTS23_TumorLesionSize_Task01_Axial_Test
|
| 255 |
+
MSD_MaskSize_Task01_Sagittal_Test
|
| 256 |
+
MSD_MaskSize_Task01_Coronal_Test
|
| 257 |
+
MSD_MaskSize_Task01_Axial_Test
|
| 258 |
+
MSD_MaskSize_Task02_Sagittal_Test
|
| 259 |
+
MSD_MaskSize_Task02_Coronal_Test
|
| 260 |
+
MSD_MaskSize_Task02_Axial_Test
|
| 261 |
+
MSD_MaskSize_Task03_Sagittal_Test
|
| 262 |
+
MSD_MaskSize_Task03_Coronal_Test
|
| 263 |
+
MSD_MaskSize_Task03_Axial_Test
|
| 264 |
+
MSD_MaskSize_Task04_Sagittal_Test
|
| 265 |
+
MSD_MaskSize_Task04_Coronal_Test
|
| 266 |
+
MSD_MaskSize_Task04_Axial_Test
|
| 267 |
+
MSD_MaskSize_Task05_Sagittal_Test
|
| 268 |
+
MSD_MaskSize_Task05_Coronal_Test
|
| 269 |
+
MSD_MaskSize_Task05_Axial_Test
|
| 270 |
+
MSD_MaskSize_Task06_Sagittal_Test
|
| 271 |
+
MSD_MaskSize_Task06_Coronal_Test
|
| 272 |
+
MSD_MaskSize_Task06_Axial_Test
|
| 273 |
+
MSD_MaskSize_Task07_Sagittal_Test
|
| 274 |
+
MSD_MaskSize_Task07_Coronal_Test
|
| 275 |
+
MSD_MaskSize_Task07_Axial_Test
|
| 276 |
+
MSD_MaskSize_Task08_Sagittal_Test
|
| 277 |
+
MSD_MaskSize_Task08_Coronal_Test
|
| 278 |
+
MSD_MaskSize_Task08_Axial_Test
|
| 279 |
+
MSD_MaskSize_Task09_Sagittal_Test
|
| 280 |
+
MSD_MaskSize_Task09_Coronal_Test
|
| 281 |
+
MSD_MaskSize_Task09_Axial_Test
|
| 282 |
+
MSD_MaskSize_Task10_Sagittal_Test
|
| 283 |
+
MSD_MaskSize_Task10_Coronal_Test
|
| 284 |
+
MSD_MaskSize_Task10_Axial_Test
|
| 285 |
+
MSD_MaskSize_Task11_Sagittal_Test
|
| 286 |
+
MSD_MaskSize_Task11_Coronal_Test
|
| 287 |
+
MSD_MaskSize_Task11_Axial_Test
|
| 288 |
+
MSD_MaskSize_Task12_Sagittal_Test
|
| 289 |
+
MSD_MaskSize_Task12_Coronal_Test
|
| 290 |
+
MSD_MaskSize_Task12_Axial_Test
|
| 291 |
+
MSD_MaskSize_Task13_Sagittal_Test
|
| 292 |
+
MSD_MaskSize_Task13_Coronal_Test
|
| 293 |
+
MSD_MaskSize_Task13_Axial_Test
|
| 294 |
+
MSD_MaskSize_Task14_Sagittal_Test
|
| 295 |
+
MSD_MaskSize_Task14_Coronal_Test
|
| 296 |
+
MSD_MaskSize_Task14_Axial_Test
|
| 297 |
+
MSD_BoxSize_Task01_Sagittal_Test
|
| 298 |
+
MSD_BoxSize_Task01_Coronal_Test
|
| 299 |
+
MSD_BoxSize_Task01_Axial_Test
|
| 300 |
+
MSD_BoxSize_Task02_Sagittal_Test
|
| 301 |
+
MSD_BoxSize_Task02_Coronal_Test
|
| 302 |
+
MSD_BoxSize_Task02_Axial_Test
|
| 303 |
+
MSD_BoxSize_Task03_Sagittal_Test
|
| 304 |
+
MSD_BoxSize_Task03_Coronal_Test
|
| 305 |
+
MSD_BoxSize_Task03_Axial_Test
|
| 306 |
+
MSD_BoxSize_Task04_Sagittal_Test
|
| 307 |
+
MSD_BoxSize_Task04_Coronal_Test
|
| 308 |
+
MSD_BoxSize_Task04_Axial_Test
|
| 309 |
+
MSD_BoxSize_Task05_Sagittal_Test
|
| 310 |
+
MSD_BoxSize_Task05_Coronal_Test
|
| 311 |
+
MSD_BoxSize_Task05_Axial_Test
|
| 312 |
+
MSD_BoxSize_Task06_Sagittal_Test
|
| 313 |
+
MSD_BoxSize_Task06_Coronal_Test
|
| 314 |
+
MSD_BoxSize_Task06_Axial_Test
|
| 315 |
+
MSD_BoxSize_Task07_Sagittal_Test
|
| 316 |
+
MSD_BoxSize_Task07_Coronal_Test
|
| 317 |
+
MSD_BoxSize_Task07_Axial_Test
|
| 318 |
+
MSD_BoxSize_Task08_Sagittal_Test
|
| 319 |
+
MSD_BoxSize_Task08_Coronal_Test
|
| 320 |
+
MSD_BoxSize_Task08_Axial_Test
|
| 321 |
+
MSD_BoxSize_Task09_Sagittal_Test
|
| 322 |
+
MSD_BoxSize_Task09_Coronal_Test
|
| 323 |
+
MSD_BoxSize_Task09_Axial_Test
|
| 324 |
+
MSD_BoxSize_Task10_Sagittal_Test
|
| 325 |
+
MSD_BoxSize_Task10_Coronal_Test
|
| 326 |
+
MSD_BoxSize_Task10_Axial_Test
|
| 327 |
+
MSD_BoxSize_Task11_Sagittal_Test
|
| 328 |
+
MSD_BoxSize_Task11_Coronal_Test
|
| 329 |
+
MSD_BoxSize_Task11_Axial_Test
|
| 330 |
+
MSD_BoxSize_Task12_Sagittal_Test
|
| 331 |
+
MSD_BoxSize_Task12_Coronal_Test
|
| 332 |
+
MSD_BoxSize_Task12_Axial_Test
|
| 333 |
+
MSD_BoxSize_Task13_Sagittal_Test
|
| 334 |
+
MSD_BoxSize_Task13_Coronal_Test
|
| 335 |
+
MSD_BoxSize_Task13_Axial_Test
|
| 336 |
+
MSD_BoxSize_Task14_Sagittal_Test
|
| 337 |
+
MSD_BoxSize_Task14_Coronal_Test
|
| 338 |
+
MSD_BoxSize_Task14_Axial_Test
|
| 339 |
+
MSD_TumorLesionSize_Task01_Sagittal_Test
|
| 340 |
+
MSD_TumorLesionSize_Task01_Coronal_Test
|
| 341 |
+
MSD_TumorLesionSize_Task01_Axial_Test
|
| 342 |
+
MSD_TumorLesionSize_Task02_Sagittal_Test
|
| 343 |
+
MSD_TumorLesionSize_Task02_Coronal_Test
|
| 344 |
+
MSD_TumorLesionSize_Task02_Axial_Test
|
| 345 |
+
MSD_TumorLesionSize_Task03_Sagittal_Test
|
| 346 |
+
MSD_TumorLesionSize_Task03_Coronal_Test
|
| 347 |
+
MSD_TumorLesionSize_Task03_Axial_Test
|
| 348 |
+
MSD_TumorLesionSize_Task04_Sagittal_Test
|
| 349 |
+
MSD_TumorLesionSize_Task04_Coronal_Test
|
| 350 |
+
MSD_TumorLesionSize_Task04_Axial_Test
|
| 351 |
+
MSD_TumorLesionSize_Task05_Sagittal_Test
|
| 352 |
+
MSD_TumorLesionSize_Task05_Coronal_Test
|
| 353 |
+
MSD_TumorLesionSize_Task05_Axial_Test
|
| 354 |
+
MSD_TumorLesionSize_Task06_Sagittal_Test
|
| 355 |
+
MSD_TumorLesionSize_Task06_Coronal_Test
|
| 356 |
+
MSD_TumorLesionSize_Task06_Axial_Test
|
| 357 |
+
MSD_TumorLesionSize_Task07_Sagittal_Test
|
| 358 |
+
MSD_TumorLesionSize_Task07_Coronal_Test
|
| 359 |
+
MSD_TumorLesionSize_Task07_Axial_Test
|
| 360 |
+
MSD_TumorLesionSize_Task08_Sagittal_Test
|
| 361 |
+
MSD_TumorLesionSize_Task08_Coronal_Test
|
| 362 |
+
MSD_TumorLesionSize_Task08_Axial_Test
|
| 363 |
+
OAIZIB-CM_MaskSize_Task01_Sagittal_Test
|
| 364 |
+
OAIZIB-CM_MaskSize_Task01_Coronal_Test
|
| 365 |
+
OAIZIB-CM_MaskSize_Task01_Axial_Test
|
| 366 |
+
OAIZIB-CM_BoxSize_Task01_Sagittal_Test
|
| 367 |
+
OAIZIB-CM_BoxSize_Task01_Coronal_Test
|
| 368 |
+
OAIZIB-CM_BoxSize_Task01_Axial_Test
|
| 369 |
+
SKM-TEA_MaskSize_Task01_Sagittal_Test
|
| 370 |
+
SKM-TEA_MaskSize_Task01_Coronal_Test
|
| 371 |
+
SKM-TEA_MaskSize_Task01_Axial_Test
|
| 372 |
+
SKM-TEA_MaskSize_Task02_Sagittal_Test
|
| 373 |
+
SKM-TEA_MaskSize_Task02_Coronal_Test
|
| 374 |
+
SKM-TEA_MaskSize_Task02_Axial_Test
|
| 375 |
+
SKM-TEA_BoxSize_Task01_Sagittal_Test
|
| 376 |
+
SKM-TEA_BoxSize_Task01_Coronal_Test
|
| 377 |
+
SKM-TEA_BoxSize_Task01_Axial_Test
|
| 378 |
+
SKM-TEA_BoxSize_Task02_Sagittal_Test
|
| 379 |
+
SKM-TEA_BoxSize_Task02_Coronal_Test
|
| 380 |
+
SKM-TEA_BoxSize_Task02_Axial_Test
|
| 381 |
+
ToothFairy2_MaskSize_Task01_Sagittal_Test
|
| 382 |
+
ToothFairy2_MaskSize_Task01_Coronal_Test
|
| 383 |
+
ToothFairy2_MaskSize_Task01_Axial_Test
|
| 384 |
+
ToothFairy2_BoxSize_Task01_Sagittal_Test
|
| 385 |
+
ToothFairy2_BoxSize_Task01_Coronal_Test
|
| 386 |
+
ToothFairy2_BoxSize_Task01_Axial_Test
|
| 387 |
+
TopCoW24_MaskSize_Task01_Sagittal_Test
|
| 388 |
+
TopCoW24_MaskSize_Task01_Coronal_Test
|
| 389 |
+
TopCoW24_MaskSize_Task01_Axial_Test
|
| 390 |
+
TopCoW24_MaskSize_Task02_Sagittal_Test
|
| 391 |
+
TopCoW24_MaskSize_Task02_Coronal_Test
|
| 392 |
+
TopCoW24_MaskSize_Task02_Axial_Test
|
| 393 |
+
TopCoW24_BoxSize_Task01_Sagittal_Test
|
| 394 |
+
TopCoW24_BoxSize_Task01_Coronal_Test
|
| 395 |
+
TopCoW24_BoxSize_Task01_Axial_Test
|
| 396 |
+
TopCoW24_BoxSize_Task02_Sagittal_Test
|
| 397 |
+
TopCoW24_BoxSize_Task02_Coronal_Test
|
| 398 |
+
TopCoW24_BoxSize_Task02_Axial_Test
|
| 399 |
+
TotalSegmentator_MaskSize_Task01_Sagittal_Test
|
| 400 |
+
TotalSegmentator_MaskSize_Task01_Coronal_Test
|
| 401 |
+
TotalSegmentator_MaskSize_Task01_Axial_Test
|
| 402 |
+
TotalSegmentator_MaskSize_Task02_Sagittal_Test
|
| 403 |
+
TotalSegmentator_MaskSize_Task02_Coronal_Test
|
| 404 |
+
TotalSegmentator_MaskSize_Task02_Axial_Test
|
| 405 |
+
TotalSegmentator_BoxSize_Task01_Sagittal_Test
|
| 406 |
+
TotalSegmentator_BoxSize_Task01_Coronal_Test
|
| 407 |
+
TotalSegmentator_BoxSize_Task01_Axial_Test
|
| 408 |
+
TotalSegmentator_BoxSize_Task02_Sagittal_Test
|
| 409 |
+
TotalSegmentator_BoxSize_Task02_Coronal_Test
|
| 410 |
+
TotalSegmentator_BoxSize_Task02_Axial_Test
|
info/ConfigurationsList_Train.csv
ADDED
|
@@ -0,0 +1,410 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
AbdomenAtlas1.0Mini_MaskSize_Task01_Sagittal_Train
|
| 2 |
+
AbdomenAtlas1.0Mini_MaskSize_Task01_Coronal_Train
|
| 3 |
+
AbdomenAtlas1.0Mini_MaskSize_Task01_Axial_Train
|
| 4 |
+
AbdomenAtlas1.0Mini_BoxSize_Task01_Sagittal_Train
|
| 5 |
+
AbdomenAtlas1.0Mini_BoxSize_Task01_Coronal_Train
|
| 6 |
+
AbdomenAtlas1.0Mini_BoxSize_Task01_Axial_Train
|
| 7 |
+
AbdomenCT-1K_MaskSize_Task01_Sagittal_Train
|
| 8 |
+
AbdomenCT-1K_MaskSize_Task01_Coronal_Train
|
| 9 |
+
AbdomenCT-1K_MaskSize_Task01_Axial_Train
|
| 10 |
+
AbdomenCT-1K_BoxSize_Task01_Sagittal_Train
|
| 11 |
+
AbdomenCT-1K_BoxSize_Task01_Coronal_Train
|
| 12 |
+
AbdomenCT-1K_BoxSize_Task01_Axial_Train
|
| 13 |
+
ACDC_MaskSize_Task01_Sagittal_Train
|
| 14 |
+
ACDC_MaskSize_Task01_Coronal_Train
|
| 15 |
+
ACDC_MaskSize_Task01_Axial_Train
|
| 16 |
+
ACDC_BoxSize_Task01_Sagittal_Train
|
| 17 |
+
ACDC_BoxSize_Task01_Coronal_Train
|
| 18 |
+
ACDC_BoxSize_Task01_Axial_Train
|
| 19 |
+
AMOS22_MaskSize_Task01_Sagittal_Train
|
| 20 |
+
AMOS22_MaskSize_Task01_Coronal_Train
|
| 21 |
+
AMOS22_MaskSize_Task01_Axial_Train
|
| 22 |
+
AMOS22_MaskSize_Task02_Sagittal_Train
|
| 23 |
+
AMOS22_MaskSize_Task02_Coronal_Train
|
| 24 |
+
AMOS22_MaskSize_Task02_Axial_Train
|
| 25 |
+
AMOS22_BoxSize_Task01_Sagittal_Train
|
| 26 |
+
AMOS22_BoxSize_Task01_Coronal_Train
|
| 27 |
+
AMOS22_BoxSize_Task01_Axial_Train
|
| 28 |
+
AMOS22_BoxSize_Task02_Sagittal_Train
|
| 29 |
+
AMOS22_BoxSize_Task02_Coronal_Train
|
| 30 |
+
AMOS22_BoxSize_Task02_Axial_Train
|
| 31 |
+
autoPET-III_MaskSize_Task01_Sagittal_Train
|
| 32 |
+
autoPET-III_MaskSize_Task01_Coronal_Train
|
| 33 |
+
autoPET-III_MaskSize_Task01_Axial_Train
|
| 34 |
+
autoPET-III_MaskSize_Task02_Sagittal_Train
|
| 35 |
+
autoPET-III_MaskSize_Task02_Coronal_Train
|
| 36 |
+
autoPET-III_MaskSize_Task02_Axial_Train
|
| 37 |
+
autoPET-III_BoxSize_Task01_Sagittal_Train
|
| 38 |
+
autoPET-III_BoxSize_Task01_Coronal_Train
|
| 39 |
+
autoPET-III_BoxSize_Task01_Axial_Train
|
| 40 |
+
autoPET-III_BoxSize_Task02_Sagittal_Train
|
| 41 |
+
autoPET-III_BoxSize_Task02_Coronal_Train
|
| 42 |
+
autoPET-III_BoxSize_Task02_Axial_Train
|
| 43 |
+
autoPET-III_TumorLesionSize_Task01_Sagittal_Train
|
| 44 |
+
autoPET-III_TumorLesionSize_Task01_Coronal_Train
|
| 45 |
+
autoPET-III_TumorLesionSize_Task01_Axial_Train
|
| 46 |
+
BCV15_MaskSize_Task01_Sagittal_Train
|
| 47 |
+
BCV15_MaskSize_Task01_Coronal_Train
|
| 48 |
+
BCV15_MaskSize_Task01_Axial_Train
|
| 49 |
+
BCV15_MaskSize_Task02_Sagittal_Train
|
| 50 |
+
BCV15_MaskSize_Task02_Coronal_Train
|
| 51 |
+
BCV15_MaskSize_Task02_Axial_Train
|
| 52 |
+
BCV15_BoxSize_Task01_Sagittal_Train
|
| 53 |
+
BCV15_BoxSize_Task01_Coronal_Train
|
| 54 |
+
BCV15_BoxSize_Task01_Axial_Train
|
| 55 |
+
BCV15_BoxSize_Task02_Sagittal_Train
|
| 56 |
+
BCV15_BoxSize_Task02_Coronal_Train
|
| 57 |
+
BCV15_BoxSize_Task02_Axial_Train
|
| 58 |
+
BraTS24_MaskSize_Task01_Sagittal_Train
|
| 59 |
+
BraTS24_MaskSize_Task01_Coronal_Train
|
| 60 |
+
BraTS24_MaskSize_Task01_Axial_Train
|
| 61 |
+
BraTS24_MaskSize_Task02_Sagittal_Train
|
| 62 |
+
BraTS24_MaskSize_Task02_Coronal_Train
|
| 63 |
+
BraTS24_MaskSize_Task02_Axial_Train
|
| 64 |
+
BraTS24_MaskSize_Task03_Sagittal_Train
|
| 65 |
+
BraTS24_MaskSize_Task03_Coronal_Train
|
| 66 |
+
BraTS24_MaskSize_Task03_Axial_Train
|
| 67 |
+
BraTS24_MaskSize_Task04_Sagittal_Train
|
| 68 |
+
BraTS24_MaskSize_Task04_Coronal_Train
|
| 69 |
+
BraTS24_MaskSize_Task04_Axial_Train
|
| 70 |
+
BraTS24_MaskSize_Task05_Sagittal_Train
|
| 71 |
+
BraTS24_MaskSize_Task05_Coronal_Train
|
| 72 |
+
BraTS24_MaskSize_Task05_Axial_Train
|
| 73 |
+
BraTS24_MaskSize_Task06_Sagittal_Train
|
| 74 |
+
BraTS24_MaskSize_Task06_Coronal_Train
|
| 75 |
+
BraTS24_MaskSize_Task06_Axial_Train
|
| 76 |
+
BraTS24_MaskSize_Task07_Sagittal_Train
|
| 77 |
+
BraTS24_MaskSize_Task07_Coronal_Train
|
| 78 |
+
BraTS24_MaskSize_Task07_Axial_Train
|
| 79 |
+
BraTS24_MaskSize_Task08_Sagittal_Train
|
| 80 |
+
BraTS24_MaskSize_Task08_Coronal_Train
|
| 81 |
+
BraTS24_MaskSize_Task08_Axial_Train
|
| 82 |
+
BraTS24_MaskSize_Task09_Sagittal_Train
|
| 83 |
+
BraTS24_MaskSize_Task09_Coronal_Train
|
| 84 |
+
BraTS24_MaskSize_Task09_Axial_Train
|
| 85 |
+
BraTS24_MaskSize_Task10_Sagittal_Train
|
| 86 |
+
BraTS24_MaskSize_Task10_Coronal_Train
|
| 87 |
+
BraTS24_MaskSize_Task10_Axial_Train
|
| 88 |
+
BraTS24_MaskSize_Task11_Sagittal_Train
|
| 89 |
+
BraTS24_MaskSize_Task11_Coronal_Train
|
| 90 |
+
BraTS24_MaskSize_Task11_Axial_Train
|
| 91 |
+
BraTS24_MaskSize_Task12_Sagittal_Train
|
| 92 |
+
BraTS24_MaskSize_Task12_Coronal_Train
|
| 93 |
+
BraTS24_MaskSize_Task12_Axial_Train
|
| 94 |
+
BraTS24_MaskSize_Task13_Sagittal_Train
|
| 95 |
+
BraTS24_MaskSize_Task13_Coronal_Train
|
| 96 |
+
BraTS24_MaskSize_Task13_Axial_Train
|
| 97 |
+
BraTS24_BoxSize_Task01_Sagittal_Train
|
| 98 |
+
BraTS24_BoxSize_Task01_Coronal_Train
|
| 99 |
+
BraTS24_BoxSize_Task01_Axial_Train
|
| 100 |
+
BraTS24_BoxSize_Task02_Sagittal_Train
|
| 101 |
+
BraTS24_BoxSize_Task02_Coronal_Train
|
| 102 |
+
BraTS24_BoxSize_Task02_Axial_Train
|
| 103 |
+
BraTS24_BoxSize_Task03_Sagittal_Train
|
| 104 |
+
BraTS24_BoxSize_Task03_Coronal_Train
|
| 105 |
+
BraTS24_BoxSize_Task03_Axial_Train
|
| 106 |
+
BraTS24_BoxSize_Task04_Sagittal_Train
|
| 107 |
+
BraTS24_BoxSize_Task04_Coronal_Train
|
| 108 |
+
BraTS24_BoxSize_Task04_Axial_Train
|
| 109 |
+
BraTS24_BoxSize_Task05_Sagittal_Train
|
| 110 |
+
BraTS24_BoxSize_Task05_Coronal_Train
|
| 111 |
+
BraTS24_BoxSize_Task05_Axial_Train
|
| 112 |
+
BraTS24_BoxSize_Task06_Sagittal_Train
|
| 113 |
+
BraTS24_BoxSize_Task06_Coronal_Train
|
| 114 |
+
BraTS24_BoxSize_Task06_Axial_Train
|
| 115 |
+
BraTS24_BoxSize_Task07_Sagittal_Train
|
| 116 |
+
BraTS24_BoxSize_Task07_Coronal_Train
|
| 117 |
+
BraTS24_BoxSize_Task07_Axial_Train
|
| 118 |
+
BraTS24_BoxSize_Task08_Sagittal_Train
|
| 119 |
+
BraTS24_BoxSize_Task08_Coronal_Train
|
| 120 |
+
BraTS24_BoxSize_Task08_Axial_Train
|
| 121 |
+
BraTS24_BoxSize_Task09_Sagittal_Train
|
| 122 |
+
BraTS24_BoxSize_Task09_Coronal_Train
|
| 123 |
+
BraTS24_BoxSize_Task09_Axial_Train
|
| 124 |
+
BraTS24_BoxSize_Task10_Sagittal_Train
|
| 125 |
+
BraTS24_BoxSize_Task10_Coronal_Train
|
| 126 |
+
BraTS24_BoxSize_Task10_Axial_Train
|
| 127 |
+
BraTS24_BoxSize_Task11_Sagittal_Train
|
| 128 |
+
BraTS24_BoxSize_Task11_Coronal_Train
|
| 129 |
+
BraTS24_BoxSize_Task11_Axial_Train
|
| 130 |
+
BraTS24_BoxSize_Task12_Sagittal_Train
|
| 131 |
+
BraTS24_BoxSize_Task12_Coronal_Train
|
| 132 |
+
BraTS24_BoxSize_Task12_Axial_Train
|
| 133 |
+
BraTS24_BoxSize_Task13_Sagittal_Train
|
| 134 |
+
BraTS24_BoxSize_Task13_Coronal_Train
|
| 135 |
+
BraTS24_BoxSize_Task13_Axial_Train
|
| 136 |
+
BraTS24_TumorLesionSize_Task01_Sagittal_Train
|
| 137 |
+
BraTS24_TumorLesionSize_Task01_Coronal_Train
|
| 138 |
+
BraTS24_TumorLesionSize_Task01_Axial_Train
|
| 139 |
+
BraTS24_TumorLesionSize_Task02_Sagittal_Train
|
| 140 |
+
BraTS24_TumorLesionSize_Task02_Coronal_Train
|
| 141 |
+
BraTS24_TumorLesionSize_Task02_Axial_Train
|
| 142 |
+
BraTS24_TumorLesionSize_Task03_Sagittal_Train
|
| 143 |
+
BraTS24_TumorLesionSize_Task03_Coronal_Train
|
| 144 |
+
BraTS24_TumorLesionSize_Task03_Axial_Train
|
| 145 |
+
BraTS24_TumorLesionSize_Task04_Sagittal_Train
|
| 146 |
+
BraTS24_TumorLesionSize_Task04_Coronal_Train
|
| 147 |
+
BraTS24_TumorLesionSize_Task04_Axial_Train
|
| 148 |
+
BraTS24_TumorLesionSize_Task05_Sagittal_Train
|
| 149 |
+
BraTS24_TumorLesionSize_Task05_Coronal_Train
|
| 150 |
+
BraTS24_TumorLesionSize_Task05_Axial_Train
|
| 151 |
+
BraTS24_TumorLesionSize_Task06_Sagittal_Train
|
| 152 |
+
BraTS24_TumorLesionSize_Task06_Coronal_Train
|
| 153 |
+
BraTS24_TumorLesionSize_Task06_Axial_Train
|
| 154 |
+
BraTS24_TumorLesionSize_Task07_Sagittal_Train
|
| 155 |
+
BraTS24_TumorLesionSize_Task07_Coronal_Train
|
| 156 |
+
BraTS24_TumorLesionSize_Task07_Axial_Train
|
| 157 |
+
BraTS24_TumorLesionSize_Task08_Sagittal_Train
|
| 158 |
+
BraTS24_TumorLesionSize_Task08_Coronal_Train
|
| 159 |
+
BraTS24_TumorLesionSize_Task08_Axial_Train
|
| 160 |
+
BraTS24_TumorLesionSize_Task09_Sagittal_Train
|
| 161 |
+
BraTS24_TumorLesionSize_Task09_Coronal_Train
|
| 162 |
+
BraTS24_TumorLesionSize_Task09_Axial_Train
|
| 163 |
+
BraTS24_TumorLesionSize_Task10_Sagittal_Train
|
| 164 |
+
BraTS24_TumorLesionSize_Task10_Coronal_Train
|
| 165 |
+
BraTS24_TumorLesionSize_Task10_Axial_Train
|
| 166 |
+
BraTS24_TumorLesionSize_Task11_Sagittal_Train
|
| 167 |
+
BraTS24_TumorLesionSize_Task11_Coronal_Train
|
| 168 |
+
BraTS24_TumorLesionSize_Task11_Axial_Train
|
| 169 |
+
BraTS24_TumorLesionSize_Task12_Sagittal_Train
|
| 170 |
+
BraTS24_TumorLesionSize_Task12_Coronal_Train
|
| 171 |
+
BraTS24_TumorLesionSize_Task12_Axial_Train
|
| 172 |
+
CAMUS_MaskSize_Task01_Sagittal_Train
|
| 173 |
+
CAMUS_MaskSize_Task01_Coronal_Train
|
| 174 |
+
CAMUS_MaskSize_Task01_Axial_Train
|
| 175 |
+
CAMUS_BoxSize_Task01_Sagittal_Train
|
| 176 |
+
CAMUS_BoxSize_Task01_Coronal_Train
|
| 177 |
+
CAMUS_BoxSize_Task01_Axial_Train
|
| 178 |
+
Ceph-Biometrics-400_BiometricsFromLandmarks_Distance_Task01_Sagittal_Train
|
| 179 |
+
Ceph-Biometrics-400_BiometricsFromLandmarks_Angle_Task01_Sagittal_Train
|
| 180 |
+
CrossMoDA_MaskSize_Task01_Sagittal_Train
|
| 181 |
+
CrossMoDA_MaskSize_Task01_Coronal_Train
|
| 182 |
+
CrossMoDA_MaskSize_Task01_Axial_Train
|
| 183 |
+
CrossMoDA_BoxSize_Task01_Sagittal_Train
|
| 184 |
+
CrossMoDA_BoxSize_Task01_Coronal_Train
|
| 185 |
+
CrossMoDA_BoxSize_Task01_Axial_Train
|
| 186 |
+
FeTA24_MaskSize_Task01_Sagittal_Train
|
| 187 |
+
FeTA24_MaskSize_Task01_Coronal_Train
|
| 188 |
+
FeTA24_MaskSize_Task01_Axial_Train
|
| 189 |
+
FeTA24_BoxSize_Task01_Sagittal_Train
|
| 190 |
+
FeTA24_BoxSize_Task01_Coronal_Train
|
| 191 |
+
FeTA24_BoxSize_Task01_Axial_Train
|
| 192 |
+
FeTA24_BiometricsFromLandmarks_Task01_Sagittal_Train
|
| 193 |
+
FeTA24_BiometricsFromLandmarks_Task01_Coronal_Train
|
| 194 |
+
FeTA24_BiometricsFromLandmarks_Task01_Axial_Train
|
| 195 |
+
FLARE22_MaskSize_Task01_Sagittal_Train
|
| 196 |
+
FLARE22_MaskSize_Task01_Coronal_Train
|
| 197 |
+
FLARE22_MaskSize_Task01_Axial_Train
|
| 198 |
+
FLARE22_BoxSize_Task01_Sagittal_Train
|
| 199 |
+
FLARE22_BoxSize_Task01_Coronal_Train
|
| 200 |
+
FLARE22_BoxSize_Task01_Axial_Train
|
| 201 |
+
HNTSMRG24_MaskSize_Task01_Sagittal_Train
|
| 202 |
+
HNTSMRG24_MaskSize_Task01_Coronal_Train
|
| 203 |
+
HNTSMRG24_MaskSize_Task01_Axial_Train
|
| 204 |
+
HNTSMRG24_MaskSize_Task02_Sagittal_Train
|
| 205 |
+
HNTSMRG24_MaskSize_Task02_Coronal_Train
|
| 206 |
+
HNTSMRG24_MaskSize_Task02_Axial_Train
|
| 207 |
+
HNTSMRG24_BoxSize_Task01_Sagittal_Train
|
| 208 |
+
HNTSMRG24_BoxSize_Task01_Coronal_Train
|
| 209 |
+
HNTSMRG24_BoxSize_Task01_Axial_Train
|
| 210 |
+
HNTSMRG24_BoxSize_Task02_Sagittal_Train
|
| 211 |
+
HNTSMRG24_BoxSize_Task02_Coronal_Train
|
| 212 |
+
HNTSMRG24_BoxSize_Task02_Axial_Train
|
| 213 |
+
HNTSMRG24_TumorLesionSize_Task01_Sagittal_Train
|
| 214 |
+
HNTSMRG24_TumorLesionSize_Task01_Coronal_Train
|
| 215 |
+
HNTSMRG24_TumorLesionSize_Task01_Axial_Train
|
| 216 |
+
HNTSMRG24_TumorLesionSize_Task02_Sagittal_Train
|
| 217 |
+
HNTSMRG24_TumorLesionSize_Task02_Coronal_Train
|
| 218 |
+
HNTSMRG24_TumorLesionSize_Task02_Axial_Train
|
| 219 |
+
HNTSMRG24_TumorLesionSize_Task03_Sagittal_Train
|
| 220 |
+
HNTSMRG24_TumorLesionSize_Task03_Coronal_Train
|
| 221 |
+
HNTSMRG24_TumorLesionSize_Task03_Axial_Train
|
| 222 |
+
HNTSMRG24_TumorLesionSize_Task04_Sagittal_Train
|
| 223 |
+
HNTSMRG24_TumorLesionSize_Task04_Coronal_Train
|
| 224 |
+
HNTSMRG24_TumorLesionSize_Task04_Axial_Train
|
| 225 |
+
ISLES24_MaskSize_Task01_Sagittal_Train
|
| 226 |
+
ISLES24_MaskSize_Task01_Coronal_Train
|
| 227 |
+
ISLES24_MaskSize_Task01_Axial_Train
|
| 228 |
+
ISLES24_MaskSize_Task02_Sagittal_Train
|
| 229 |
+
ISLES24_MaskSize_Task02_Coronal_Train
|
| 230 |
+
ISLES24_MaskSize_Task02_Axial_Train
|
| 231 |
+
ISLES24_BoxSize_Task01_Sagittal_Train
|
| 232 |
+
ISLES24_BoxSize_Task01_Coronal_Train
|
| 233 |
+
ISLES24_BoxSize_Task01_Axial_Train
|
| 234 |
+
ISLES24_BoxSize_Task02_Sagittal_Train
|
| 235 |
+
ISLES24_BoxSize_Task02_Coronal_Train
|
| 236 |
+
ISLES24_BoxSize_Task02_Axial_Train
|
| 237 |
+
KiPA22_MaskSize_Task01_Sagittal_Train
|
| 238 |
+
KiPA22_MaskSize_Task01_Coronal_Train
|
| 239 |
+
KiPA22_MaskSize_Task01_Axial_Train
|
| 240 |
+
KiPA22_BoxSize_Task01_Sagittal_Train
|
| 241 |
+
KiPA22_BoxSize_Task01_Coronal_Train
|
| 242 |
+
KiPA22_BoxSize_Task01_Axial_Train
|
| 243 |
+
KiPA22_TumorLesionSize_Task01_Sagittal_Train
|
| 244 |
+
KiPA22_TumorLesionSize_Task01_Coronal_Train
|
| 245 |
+
KiPA22_TumorLesionSize_Task01_Axial_Train
|
| 246 |
+
KiTS23_MaskSize_Task01_Sagittal_Train
|
| 247 |
+
KiTS23_MaskSize_Task01_Coronal_Train
|
| 248 |
+
KiTS23_MaskSize_Task01_Axial_Train
|
| 249 |
+
KiTS23_BoxSize_Task01_Sagittal_Train
|
| 250 |
+
KiTS23_BoxSize_Task01_Coronal_Train
|
| 251 |
+
KiTS23_BoxSize_Task01_Axial_Train
|
| 252 |
+
KiTS23_TumorLesionSize_Task01_Sagittal_Train
|
| 253 |
+
KiTS23_TumorLesionSize_Task01_Coronal_Train
|
| 254 |
+
KiTS23_TumorLesionSize_Task01_Axial_Train
|
| 255 |
+
MSD_MaskSize_Task01_Sagittal_Train
|
| 256 |
+
MSD_MaskSize_Task01_Coronal_Train
|
| 257 |
+
MSD_MaskSize_Task01_Axial_Train
|
| 258 |
+
MSD_MaskSize_Task02_Sagittal_Train
|
| 259 |
+
MSD_MaskSize_Task02_Coronal_Train
|
| 260 |
+
MSD_MaskSize_Task02_Axial_Train
|
| 261 |
+
MSD_MaskSize_Task03_Sagittal_Train
|
| 262 |
+
MSD_MaskSize_Task03_Coronal_Train
|
| 263 |
+
MSD_MaskSize_Task03_Axial_Train
|
| 264 |
+
MSD_MaskSize_Task04_Sagittal_Train
|
| 265 |
+
MSD_MaskSize_Task04_Coronal_Train
|
| 266 |
+
MSD_MaskSize_Task04_Axial_Train
|
| 267 |
+
MSD_MaskSize_Task05_Sagittal_Train
|
| 268 |
+
MSD_MaskSize_Task05_Coronal_Train
|
| 269 |
+
MSD_MaskSize_Task05_Axial_Train
|
| 270 |
+
MSD_MaskSize_Task06_Sagittal_Train
|
| 271 |
+
MSD_MaskSize_Task06_Coronal_Train
|
| 272 |
+
MSD_MaskSize_Task06_Axial_Train
|
| 273 |
+
MSD_MaskSize_Task07_Sagittal_Train
|
| 274 |
+
MSD_MaskSize_Task07_Coronal_Train
|
| 275 |
+
MSD_MaskSize_Task07_Axial_Train
|
| 276 |
+
MSD_MaskSize_Task08_Sagittal_Train
|
| 277 |
+
MSD_MaskSize_Task08_Coronal_Train
|
| 278 |
+
MSD_MaskSize_Task08_Axial_Train
|
| 279 |
+
MSD_MaskSize_Task09_Sagittal_Train
|
| 280 |
+
MSD_MaskSize_Task09_Coronal_Train
|
| 281 |
+
MSD_MaskSize_Task09_Axial_Train
|
| 282 |
+
MSD_MaskSize_Task10_Sagittal_Train
|
| 283 |
+
MSD_MaskSize_Task10_Coronal_Train
|
| 284 |
+
MSD_MaskSize_Task10_Axial_Train
|
| 285 |
+
MSD_MaskSize_Task11_Sagittal_Train
|
| 286 |
+
MSD_MaskSize_Task11_Coronal_Train
|
| 287 |
+
MSD_MaskSize_Task11_Axial_Train
|
| 288 |
+
MSD_MaskSize_Task12_Sagittal_Train
|
| 289 |
+
MSD_MaskSize_Task12_Coronal_Train
|
| 290 |
+
MSD_MaskSize_Task12_Axial_Train
|
| 291 |
+
MSD_MaskSize_Task13_Sagittal_Train
|
| 292 |
+
MSD_MaskSize_Task13_Coronal_Train
|
| 293 |
+
MSD_MaskSize_Task13_Axial_Train
|
| 294 |
+
MSD_MaskSize_Task14_Sagittal_Train
|
| 295 |
+
MSD_MaskSize_Task14_Coronal_Train
|
| 296 |
+
MSD_MaskSize_Task14_Axial_Train
|
| 297 |
+
MSD_BoxSize_Task01_Sagittal_Train
|
| 298 |
+
MSD_BoxSize_Task01_Coronal_Train
|
| 299 |
+
MSD_BoxSize_Task01_Axial_Train
|
| 300 |
+
MSD_BoxSize_Task02_Sagittal_Train
|
| 301 |
+
MSD_BoxSize_Task02_Coronal_Train
|
| 302 |
+
MSD_BoxSize_Task02_Axial_Train
|
| 303 |
+
MSD_BoxSize_Task03_Sagittal_Train
|
| 304 |
+
MSD_BoxSize_Task03_Coronal_Train
|
| 305 |
+
MSD_BoxSize_Task03_Axial_Train
|
| 306 |
+
MSD_BoxSize_Task04_Sagittal_Train
|
| 307 |
+
MSD_BoxSize_Task04_Coronal_Train
|
| 308 |
+
MSD_BoxSize_Task04_Axial_Train
|
| 309 |
+
MSD_BoxSize_Task05_Sagittal_Train
|
| 310 |
+
MSD_BoxSize_Task05_Coronal_Train
|
| 311 |
+
MSD_BoxSize_Task05_Axial_Train
|
| 312 |
+
MSD_BoxSize_Task06_Sagittal_Train
|
| 313 |
+
MSD_BoxSize_Task06_Coronal_Train
|
| 314 |
+
MSD_BoxSize_Task06_Axial_Train
|
| 315 |
+
MSD_BoxSize_Task07_Sagittal_Train
|
| 316 |
+
MSD_BoxSize_Task07_Coronal_Train
|
| 317 |
+
MSD_BoxSize_Task07_Axial_Train
|
| 318 |
+
MSD_BoxSize_Task08_Sagittal_Train
|
| 319 |
+
MSD_BoxSize_Task08_Coronal_Train
|
| 320 |
+
MSD_BoxSize_Task08_Axial_Train
|
| 321 |
+
MSD_BoxSize_Task09_Sagittal_Train
|
| 322 |
+
MSD_BoxSize_Task09_Coronal_Train
|
| 323 |
+
MSD_BoxSize_Task09_Axial_Train
|
| 324 |
+
MSD_BoxSize_Task10_Sagittal_Train
|
| 325 |
+
MSD_BoxSize_Task10_Coronal_Train
|
| 326 |
+
MSD_BoxSize_Task10_Axial_Train
|
| 327 |
+
MSD_BoxSize_Task11_Sagittal_Train
|
| 328 |
+
MSD_BoxSize_Task11_Coronal_Train
|
| 329 |
+
MSD_BoxSize_Task11_Axial_Train
|
| 330 |
+
MSD_BoxSize_Task12_Sagittal_Train
|
| 331 |
+
MSD_BoxSize_Task12_Coronal_Train
|
| 332 |
+
MSD_BoxSize_Task12_Axial_Train
|
| 333 |
+
MSD_BoxSize_Task13_Sagittal_Train
|
| 334 |
+
MSD_BoxSize_Task13_Coronal_Train
|
| 335 |
+
MSD_BoxSize_Task13_Axial_Train
|
| 336 |
+
MSD_BoxSize_Task14_Sagittal_Train
|
| 337 |
+
MSD_BoxSize_Task14_Coronal_Train
|
| 338 |
+
MSD_BoxSize_Task14_Axial_Train
|
| 339 |
+
MSD_TumorLesionSize_Task01_Sagittal_Train
|
| 340 |
+
MSD_TumorLesionSize_Task01_Coronal_Train
|
| 341 |
+
MSD_TumorLesionSize_Task01_Axial_Train
|
| 342 |
+
MSD_TumorLesionSize_Task02_Sagittal_Train
|
| 343 |
+
MSD_TumorLesionSize_Task02_Coronal_Train
|
| 344 |
+
MSD_TumorLesionSize_Task02_Axial_Train
|
| 345 |
+
MSD_TumorLesionSize_Task03_Sagittal_Train
|
| 346 |
+
MSD_TumorLesionSize_Task03_Coronal_Train
|
| 347 |
+
MSD_TumorLesionSize_Task03_Axial_Train
|
| 348 |
+
MSD_TumorLesionSize_Task04_Sagittal_Train
|
| 349 |
+
MSD_TumorLesionSize_Task04_Coronal_Train
|
| 350 |
+
MSD_TumorLesionSize_Task04_Axial_Train
|
| 351 |
+
MSD_TumorLesionSize_Task05_Sagittal_Train
|
| 352 |
+
MSD_TumorLesionSize_Task05_Coronal_Train
|
| 353 |
+
MSD_TumorLesionSize_Task05_Axial_Train
|
| 354 |
+
MSD_TumorLesionSize_Task06_Sagittal_Train
|
| 355 |
+
MSD_TumorLesionSize_Task06_Coronal_Train
|
| 356 |
+
MSD_TumorLesionSize_Task06_Axial_Train
|
| 357 |
+
MSD_TumorLesionSize_Task07_Sagittal_Train
|
| 358 |
+
MSD_TumorLesionSize_Task07_Coronal_Train
|
| 359 |
+
MSD_TumorLesionSize_Task07_Axial_Train
|
| 360 |
+
MSD_TumorLesionSize_Task08_Sagittal_Train
|
| 361 |
+
MSD_TumorLesionSize_Task08_Coronal_Train
|
| 362 |
+
MSD_TumorLesionSize_Task08_Axial_Train
|
| 363 |
+
OAIZIB-CM_MaskSize_Task01_Sagittal_Train
|
| 364 |
+
OAIZIB-CM_MaskSize_Task01_Coronal_Train
|
| 365 |
+
OAIZIB-CM_MaskSize_Task01_Axial_Train
|
| 366 |
+
OAIZIB-CM_BoxSize_Task01_Sagittal_Train
|
| 367 |
+
OAIZIB-CM_BoxSize_Task01_Coronal_Train
|
| 368 |
+
OAIZIB-CM_BoxSize_Task01_Axial_Train
|
| 369 |
+
SKM-TEA_MaskSize_Task01_Sagittal_Train
|
| 370 |
+
SKM-TEA_MaskSize_Task01_Coronal_Train
|
| 371 |
+
SKM-TEA_MaskSize_Task01_Axial_Train
|
| 372 |
+
SKM-TEA_MaskSize_Task02_Sagittal_Train
|
| 373 |
+
SKM-TEA_MaskSize_Task02_Coronal_Train
|
| 374 |
+
SKM-TEA_MaskSize_Task02_Axial_Train
|
| 375 |
+
SKM-TEA_BoxSize_Task01_Sagittal_Train
|
| 376 |
+
SKM-TEA_BoxSize_Task01_Coronal_Train
|
| 377 |
+
SKM-TEA_BoxSize_Task01_Axial_Train
|
| 378 |
+
SKM-TEA_BoxSize_Task02_Sagittal_Train
|
| 379 |
+
SKM-TEA_BoxSize_Task02_Coronal_Train
|
| 380 |
+
SKM-TEA_BoxSize_Task02_Axial_Train
|
| 381 |
+
ToothFairy2_MaskSize_Task01_Sagittal_Train
|
| 382 |
+
ToothFairy2_MaskSize_Task01_Coronal_Train
|
| 383 |
+
ToothFairy2_MaskSize_Task01_Axial_Train
|
| 384 |
+
ToothFairy2_BoxSize_Task01_Sagittal_Train
|
| 385 |
+
ToothFairy2_BoxSize_Task01_Coronal_Train
|
| 386 |
+
ToothFairy2_BoxSize_Task01_Axial_Train
|
| 387 |
+
TopCoW24_MaskSize_Task01_Sagittal_Train
|
| 388 |
+
TopCoW24_MaskSize_Task01_Coronal_Train
|
| 389 |
+
TopCoW24_MaskSize_Task01_Axial_Train
|
| 390 |
+
TopCoW24_MaskSize_Task02_Sagittal_Train
|
| 391 |
+
TopCoW24_MaskSize_Task02_Coronal_Train
|
| 392 |
+
TopCoW24_MaskSize_Task02_Axial_Train
|
| 393 |
+
TopCoW24_BoxSize_Task01_Sagittal_Train
|
| 394 |
+
TopCoW24_BoxSize_Task01_Coronal_Train
|
| 395 |
+
TopCoW24_BoxSize_Task01_Axial_Train
|
| 396 |
+
TopCoW24_BoxSize_Task02_Sagittal_Train
|
| 397 |
+
TopCoW24_BoxSize_Task02_Coronal_Train
|
| 398 |
+
TopCoW24_BoxSize_Task02_Axial_Train
|
| 399 |
+
TotalSegmentator_MaskSize_Task01_Sagittal_Train
|
| 400 |
+
TotalSegmentator_MaskSize_Task01_Coronal_Train
|
| 401 |
+
TotalSegmentator_MaskSize_Task01_Axial_Train
|
| 402 |
+
TotalSegmentator_MaskSize_Task02_Sagittal_Train
|
| 403 |
+
TotalSegmentator_MaskSize_Task02_Coronal_Train
|
| 404 |
+
TotalSegmentator_MaskSize_Task02_Axial_Train
|
| 405 |
+
TotalSegmentator_BoxSize_Task01_Sagittal_Train
|
| 406 |
+
TotalSegmentator_BoxSize_Task01_Coronal_Train
|
| 407 |
+
TotalSegmentator_BoxSize_Task01_Axial_Train
|
| 408 |
+
TotalSegmentator_BoxSize_Task02_Sagittal_Train
|
| 409 |
+
TotalSegmentator_BoxSize_Task02_Coronal_Train
|
| 410 |
+
TotalSegmentator_BoxSize_Task02_Axial_Train
|
src/medvision_ds/__init__.py
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""BiometricVQA package."""
|
| 2 |
+
from . import utils
|
| 3 |
+
from . import datasets
|
| 4 |
+
from .__version__ import __version__
|
| 5 |
+
|
| 6 |
+
__all__ = ["utils", "datasets", "__version__"]
|
src/medvision_ds/__version__.py
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
__version__ = "1.0.0"
|
src/medvision_ds/datasets/ACDC/__init__.py
ADDED
|
File without changes
|
src/medvision_ds/datasets/ACDC/download_fast.py
ADDED
|
@@ -0,0 +1,102 @@
|
|
|
|
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|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
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|
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|
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|
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|
|
|
|
|
|
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|
|
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|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import shutil
|
| 3 |
+
import argparse
|
| 4 |
+
import zipfile
|
| 5 |
+
from huggingface_hub import hf_hub_download
|
| 6 |
+
from medvision_ds.utils.preprocess_utils import move_folder
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
# ====================================
|
| 10 |
+
# Dataset Info [!]
|
| 11 |
+
# ====================================
|
| 12 |
+
# Dataset: ACDC
|
| 13 |
+
# Data: https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html
|
| 14 |
+
# Format: nii.gz
|
| 15 |
+
# ====================================
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
def download_and_extract(dataset_dir, dataset_name, **kwargs):
|
| 19 |
+
"""
|
| 20 |
+
Download and extract the AbdomenAtlas dataset.
|
| 21 |
+
|
| 22 |
+
NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name
|
| 23 |
+
the other arguments must be kwargs
|
| 24 |
+
"""
|
| 25 |
+
# Download files
|
| 26 |
+
current_dir = os.getcwd()
|
| 27 |
+
os.chdir(dataset_dir)
|
| 28 |
+
tmp_dir = os.path.join(dataset_dir, "tmp")
|
| 29 |
+
os.makedirs(tmp_dir, exist_ok=True)
|
| 30 |
+
os.chdir(tmp_dir)
|
| 31 |
+
print(f"Downloading {dataset_name} dataset to {dataset_dir}...")
|
| 32 |
+
|
| 33 |
+
# ====================================
|
| 34 |
+
# Add download logic here [!]
|
| 35 |
+
# ====================================
|
| 36 |
+
# Download and extract dataset
|
| 37 |
+
for file in ["Images.zip", "Masks.zip"]:
|
| 38 |
+
# Download and extract dataset
|
| 39 |
+
hf_hub_download(
|
| 40 |
+
repo_id="YongchengYAO/ACDC",
|
| 41 |
+
filename=file,
|
| 42 |
+
repo_type="dataset",
|
| 43 |
+
revision="660f25484d2bd11d0416acdf332b97fd97f9c453", # commit hash on 2025-02-20
|
| 44 |
+
local_dir=".",
|
| 45 |
+
)
|
| 46 |
+
print(f"Extracting {file}... This may take some time.")
|
| 47 |
+
with zipfile.ZipFile(file, "r") as zip_ref:
|
| 48 |
+
zip_ref.extractall()
|
| 49 |
+
os.remove(file)
|
| 50 |
+
|
| 51 |
+
# Move folder to dataset_dir
|
| 52 |
+
folders_to_move = [
|
| 53 |
+
"Images",
|
| 54 |
+
"Masks",
|
| 55 |
+
]
|
| 56 |
+
for folder in folders_to_move:
|
| 57 |
+
move_folder(
|
| 58 |
+
os.path.join(tmp_dir, folder),
|
| 59 |
+
os.path.join(dataset_dir, folder),
|
| 60 |
+
create_dest=True,
|
| 61 |
+
)
|
| 62 |
+
# ====================================
|
| 63 |
+
|
| 64 |
+
print(f"Download and extraction completed for {dataset_name}")
|
| 65 |
+
os.chdir(dataset_dir)
|
| 66 |
+
shutil.rmtree(tmp_dir)
|
| 67 |
+
os.chdir(current_dir)
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
def main(dir_datasets_data, dataset_name, **kwargs):
|
| 71 |
+
# Create dataset directory
|
| 72 |
+
dataset_dir = os.path.join(dir_datasets_data, dataset_name)
|
| 73 |
+
os.makedirs(dataset_dir, exist_ok=True)
|
| 74 |
+
|
| 75 |
+
# Change to dataset directory
|
| 76 |
+
os.chdir(dataset_dir)
|
| 77 |
+
|
| 78 |
+
# Download and extract dataset
|
| 79 |
+
download_and_extract(dataset_dir, dataset_name, **kwargs)
|
| 80 |
+
|
| 81 |
+
|
| 82 |
+
if __name__ == "__main__":
|
| 83 |
+
# Set up argument parser
|
| 84 |
+
parser = argparse.ArgumentParser(description="Download and extract dataset")
|
| 85 |
+
parser.add_argument(
|
| 86 |
+
"-d",
|
| 87 |
+
"--dir_datasets_data",
|
| 88 |
+
help="Directory path where datasets will be stored",
|
| 89 |
+
required=True,
|
| 90 |
+
)
|
| 91 |
+
parser.add_argument(
|
| 92 |
+
"-n",
|
| 93 |
+
"--dataset_name",
|
| 94 |
+
help="Name of the dataset",
|
| 95 |
+
required=True,
|
| 96 |
+
)
|
| 97 |
+
args = parser.parse_args()
|
| 98 |
+
|
| 99 |
+
main(
|
| 100 |
+
dir_datasets_data=args.dir_datasets_data,
|
| 101 |
+
dataset_name=args.dataset_name,
|
| 102 |
+
)
|
src/medvision_ds/datasets/ACDC/download_raw.py
ADDED
|
@@ -0,0 +1,122 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import shutil
|
| 3 |
+
import argparse
|
| 4 |
+
import zipfile
|
| 5 |
+
import urllib.request
|
| 6 |
+
from medvision_ds.utils.preprocess_utils import process_dataset, move_folder
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
# ====================================
|
| 10 |
+
# Dataset Info [!]
|
| 11 |
+
# ====================================
|
| 12 |
+
# Dataset: ACDC
|
| 13 |
+
# Data: https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html
|
| 14 |
+
# Format: nii.gz
|
| 15 |
+
# ====================================
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
def download_and_extract(dataset_dir, dataset_name, **kwargs):
|
| 19 |
+
"""
|
| 20 |
+
Download and extract the AbdomenAtlas dataset.
|
| 21 |
+
|
| 22 |
+
NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name
|
| 23 |
+
the other arguments must be kwargs
|
| 24 |
+
"""
|
| 25 |
+
# Download files
|
| 26 |
+
current_dir = os.getcwd()
|
| 27 |
+
os.chdir(dataset_dir)
|
| 28 |
+
tmp_dir = os.path.join(dataset_dir, "tmp")
|
| 29 |
+
os.makedirs(tmp_dir, exist_ok=True)
|
| 30 |
+
os.chdir(tmp_dir)
|
| 31 |
+
print(f"Downloading {dataset_name} dataset to {dataset_dir}...")
|
| 32 |
+
|
| 33 |
+
# ====================================
|
| 34 |
+
# Add download logic here [!]
|
| 35 |
+
# ====================================
|
| 36 |
+
# Download dataset using pure Python
|
| 37 |
+
print("Downloading ACDC.zip...")
|
| 38 |
+
urllib.request.urlretrieve(
|
| 39 |
+
"https://humanheart-project.creatis.insa-lyon.fr/database/api/v1/collection/637218c173e9f0047faa00fb/download",
|
| 40 |
+
"ACDC.zip",
|
| 41 |
+
)
|
| 42 |
+
|
| 43 |
+
# Extract and cleanup
|
| 44 |
+
print("Extracting ACDC.zip...")
|
| 45 |
+
with zipfile.ZipFile("ACDC.zip", "r") as zip_ref:
|
| 46 |
+
zip_ref.extractall()
|
| 47 |
+
os.remove("ACDC.zip")
|
| 48 |
+
|
| 49 |
+
# Reorganize directory structure
|
| 50 |
+
shutil.move(os.path.join("ACDC", "database"), ".")
|
| 51 |
+
shutil.rmtree("ACDC")
|
| 52 |
+
|
| 53 |
+
# Remove documentation and consolidate data
|
| 54 |
+
shutil.move(os.path.join("database", "testing"), ".")
|
| 55 |
+
shutil.move(os.path.join("database", "training"), ".")
|
| 56 |
+
shutil.rmtree("database")
|
| 57 |
+
|
| 58 |
+
# Move testing contents to training
|
| 59 |
+
for item in os.listdir("testing"):
|
| 60 |
+
if item.startswith("patient"):
|
| 61 |
+
shutil.move(os.path.join("testing", item), "training")
|
| 62 |
+
shutil.rmtree("testing")
|
| 63 |
+
|
| 64 |
+
# Create directories
|
| 65 |
+
os.makedirs("Images", exist_ok=True)
|
| 66 |
+
os.makedirs("Masks", exist_ok=True)
|
| 67 |
+
|
| 68 |
+
# Process and organize files
|
| 69 |
+
process_dataset(["training"], "*_gt.nii.gz", "_gt.nii.gz", replace=False)
|
| 70 |
+
|
| 71 |
+
# Move folder to dataset_dir
|
| 72 |
+
folders_to_move = [
|
| 73 |
+
"Images",
|
| 74 |
+
"Masks",
|
| 75 |
+
]
|
| 76 |
+
for folder in folders_to_move:
|
| 77 |
+
move_folder(
|
| 78 |
+
os.path.join(tmp_dir, folder),
|
| 79 |
+
os.path.join(dataset_dir, folder),
|
| 80 |
+
create_dest=True,
|
| 81 |
+
)
|
| 82 |
+
# ====================================
|
| 83 |
+
|
| 84 |
+
print(f"Download and extraction completed for {dataset_name}")
|
| 85 |
+
os.chdir(dataset_dir)
|
| 86 |
+
shutil.rmtree(tmp_dir)
|
| 87 |
+
os.chdir(current_dir)
|
| 88 |
+
|
| 89 |
+
|
| 90 |
+
def main(dir_datasets_data, dataset_name, **kwargs):
|
| 91 |
+
# Create dataset directory
|
| 92 |
+
dataset_dir = os.path.join(dir_datasets_data, dataset_name)
|
| 93 |
+
os.makedirs(dataset_dir, exist_ok=True)
|
| 94 |
+
|
| 95 |
+
# Change to dataset directory
|
| 96 |
+
os.chdir(dataset_dir)
|
| 97 |
+
|
| 98 |
+
# Download and extract dataset
|
| 99 |
+
download_and_extract(dataset_dir, dataset_name, **kwargs)
|
| 100 |
+
|
| 101 |
+
|
| 102 |
+
if __name__ == "__main__":
|
| 103 |
+
# Set up argument parser
|
| 104 |
+
parser = argparse.ArgumentParser(description="Download and extract dataset")
|
| 105 |
+
parser.add_argument(
|
| 106 |
+
"-d",
|
| 107 |
+
"--dir_datasets_data",
|
| 108 |
+
help="Directory path where datasets will be stored",
|
| 109 |
+
required=True,
|
| 110 |
+
)
|
| 111 |
+
parser.add_argument(
|
| 112 |
+
"-n",
|
| 113 |
+
"--dataset_name",
|
| 114 |
+
help="Name of the dataset",
|
| 115 |
+
required=True,
|
| 116 |
+
)
|
| 117 |
+
args = parser.parse_args()
|
| 118 |
+
|
| 119 |
+
main(
|
| 120 |
+
dir_datasets_data=args.dir_datasets_data,
|
| 121 |
+
dataset_name=args.dataset_name,
|
| 122 |
+
)
|
src/medvision_ds/datasets/ACDC/preprocess_detection.py
ADDED
|
@@ -0,0 +1,128 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import argparse
|
| 3 |
+
from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus
|
| 4 |
+
from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection
|
| 5 |
+
|
| 6 |
+
# ====================================
|
| 7 |
+
# Dataset Info [!]
|
| 8 |
+
# Do not change keys in
|
| 9 |
+
# - benchmark_plan
|
| 10 |
+
# ====================================
|
| 11 |
+
dataset_info = {
|
| 12 |
+
"dataset": "ACDC",
|
| 13 |
+
"dataset_website": " https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html",
|
| 14 |
+
"dataset_data": [
|
| 15 |
+
"https://humanheart-project.creatis.insa-lyon.fr/database/#collection/637218c173e9f0047faa00fb",
|
| 16 |
+
],
|
| 17 |
+
"license": ["CC BY-NC-SA 4.0"],
|
| 18 |
+
"paper": ["https://doi.org/10.1109/TMI.2018.2837502"],
|
| 19 |
+
}
|
| 20 |
+
|
| 21 |
+
labels_map = {
|
| 22 |
+
"1": "right ventricular cavity",
|
| 23 |
+
"2": "myocardium",
|
| 24 |
+
"3": "left ventricular cavity",
|
| 25 |
+
}
|
| 26 |
+
|
| 27 |
+
benchmark_plan = {
|
| 28 |
+
"dataset_info": dataset_info,
|
| 29 |
+
"tasks": [
|
| 30 |
+
{
|
| 31 |
+
"image_modality": "MRI",
|
| 32 |
+
"image_description": "cardiac magnetic resonance imaging (MRI) scan",
|
| 33 |
+
"image_folder": "Images",
|
| 34 |
+
"mask_folder": "Masks",
|
| 35 |
+
"image_prefix": "",
|
| 36 |
+
"image_suffix": ".nii.gz",
|
| 37 |
+
"mask_prefix": "",
|
| 38 |
+
"mask_suffix": "_gt.nii.gz",
|
| 39 |
+
"labels_map": labels_map,
|
| 40 |
+
},
|
| 41 |
+
],
|
| 42 |
+
}
|
| 43 |
+
# ====================================
|
| 44 |
+
|
| 45 |
+
|
| 46 |
+
def main(
|
| 47 |
+
dir_datasets_data,
|
| 48 |
+
dataset_name,
|
| 49 |
+
benchmark_plan=benchmark_plan,
|
| 50 |
+
random_seed=1024,
|
| 51 |
+
split_ratio=0.7,
|
| 52 |
+
force_uint16_mask=False,
|
| 53 |
+
reorient2RAS=False,
|
| 54 |
+
):
|
| 55 |
+
# Create dataset directory
|
| 56 |
+
dataset_dir = os.path.join(dir_datasets_data, dataset_name)
|
| 57 |
+
os.makedirs(dataset_dir, exist_ok=True)
|
| 58 |
+
|
| 59 |
+
# Change to dataset directory
|
| 60 |
+
os.chdir(dataset_dir)
|
| 61 |
+
|
| 62 |
+
# Process dataset for detection task
|
| 63 |
+
planner = MedVision_BenchmarkPlannerDetection(
|
| 64 |
+
dataset_dir=dataset_dir,
|
| 65 |
+
bm_plan=benchmark_plan,
|
| 66 |
+
dataset_name=dataset_name,
|
| 67 |
+
seed=random_seed,
|
| 68 |
+
split_ratio=split_ratio,
|
| 69 |
+
force_uint16_mask=force_uint16_mask,
|
| 70 |
+
reorient2RAS=reorient2RAS,
|
| 71 |
+
num_proc=_get_cgroup_limited_cpus(),
|
| 72 |
+
)
|
| 73 |
+
planner.process()
|
| 74 |
+
|
| 75 |
+
|
| 76 |
+
if __name__ == "__main__":
|
| 77 |
+
# Set up argument parser
|
| 78 |
+
parser = argparse.ArgumentParser(
|
| 79 |
+
description="Generate benchmark planner for detection task."
|
| 80 |
+
)
|
| 81 |
+
parser.add_argument(
|
| 82 |
+
"-d",
|
| 83 |
+
"--dir_datasets_data",
|
| 84 |
+
type=str,
|
| 85 |
+
help="Directory path where datasets will be stored",
|
| 86 |
+
required=True,
|
| 87 |
+
)
|
| 88 |
+
parser.add_argument(
|
| 89 |
+
"-n",
|
| 90 |
+
"--dataset_name",
|
| 91 |
+
type=str,
|
| 92 |
+
help="Name of the dataset",
|
| 93 |
+
required=True,
|
| 94 |
+
)
|
| 95 |
+
parser.add_argument(
|
| 96 |
+
"--random_seed",
|
| 97 |
+
type=int,
|
| 98 |
+
default=1024,
|
| 99 |
+
help="Random seed for reproducibility",
|
| 100 |
+
)
|
| 101 |
+
parser.add_argument(
|
| 102 |
+
"--split_ratio",
|
| 103 |
+
type=float,
|
| 104 |
+
default=0.7,
|
| 105 |
+
help="Train/test split ratio (0-1)",
|
| 106 |
+
)
|
| 107 |
+
parser.add_argument(
|
| 108 |
+
"--force_uint16_mask",
|
| 109 |
+
action="store_true",
|
| 110 |
+
help="Force mask to be uint16",
|
| 111 |
+
)
|
| 112 |
+
parser.add_argument(
|
| 113 |
+
"--reorient2RAS",
|
| 114 |
+
action="store_true",
|
| 115 |
+
help="Reorient images and masks to RAS orientation",
|
| 116 |
+
)
|
| 117 |
+
|
| 118 |
+
args = parser.parse_args()
|
| 119 |
+
|
| 120 |
+
main(
|
| 121 |
+
benchmark_plan=benchmark_plan, # global variable
|
| 122 |
+
dir_datasets_data=args.dir_datasets_data,
|
| 123 |
+
dataset_name=args.dataset_name,
|
| 124 |
+
random_seed=args.random_seed,
|
| 125 |
+
split_ratio=args.split_ratio,
|
| 126 |
+
force_uint16_mask=args.force_uint16_mask,
|
| 127 |
+
reorient2RAS=args.reorient2RAS,
|
| 128 |
+
)
|
src/medvision_ds/datasets/ACDC/preprocess_segmentation.py
ADDED
|
@@ -0,0 +1,128 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import argparse
|
| 3 |
+
from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus
|
| 4 |
+
from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation
|
| 5 |
+
|
| 6 |
+
# ====================================
|
| 7 |
+
# Dataset Info [!]
|
| 8 |
+
# Do not change keys in
|
| 9 |
+
# - benchmark_plan
|
| 10 |
+
# ====================================
|
| 11 |
+
dataset_info = {
|
| 12 |
+
"dataset": "ACDC",
|
| 13 |
+
"dataset_website": " https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html",
|
| 14 |
+
"dataset_data": [
|
| 15 |
+
"https://humanheart-project.creatis.insa-lyon.fr/database/#collection/637218c173e9f0047faa00fb",
|
| 16 |
+
],
|
| 17 |
+
"license": ["CC BY-NC-SA 4.0"],
|
| 18 |
+
"paper": ["https://doi.org/10.1109/TMI.2018.2837502"],
|
| 19 |
+
}
|
| 20 |
+
|
| 21 |
+
labels_map = {
|
| 22 |
+
"1": "right ventricular cavity",
|
| 23 |
+
"2": "myocardium",
|
| 24 |
+
"3": "left ventricular cavity",
|
| 25 |
+
}
|
| 26 |
+
|
| 27 |
+
benchmark_plan = {
|
| 28 |
+
"dataset_info": dataset_info,
|
| 29 |
+
"tasks": [
|
| 30 |
+
{
|
| 31 |
+
"image_modality": "MRI",
|
| 32 |
+
"image_description": "cardiac magnetic resonance imaging (MRI) scan",
|
| 33 |
+
"image_folder": "Images",
|
| 34 |
+
"mask_folder": "Masks",
|
| 35 |
+
"image_prefix": "",
|
| 36 |
+
"image_suffix": ".nii.gz",
|
| 37 |
+
"mask_prefix": "",
|
| 38 |
+
"mask_suffix": "_gt.nii.gz",
|
| 39 |
+
"labels_map": labels_map,
|
| 40 |
+
},
|
| 41 |
+
],
|
| 42 |
+
}
|
| 43 |
+
# ====================================
|
| 44 |
+
|
| 45 |
+
|
| 46 |
+
def main(
|
| 47 |
+
dir_datasets_data,
|
| 48 |
+
dataset_name,
|
| 49 |
+
benchmark_plan=benchmark_plan,
|
| 50 |
+
random_seed=1024,
|
| 51 |
+
split_ratio=0.7,
|
| 52 |
+
force_uint16_mask=False,
|
| 53 |
+
reorient2RAS=False,
|
| 54 |
+
):
|
| 55 |
+
# Create dataset directory
|
| 56 |
+
dataset_dir = os.path.join(dir_datasets_data, dataset_name)
|
| 57 |
+
os.makedirs(dataset_dir, exist_ok=True)
|
| 58 |
+
|
| 59 |
+
# Change to dataset directory
|
| 60 |
+
os.chdir(dataset_dir)
|
| 61 |
+
|
| 62 |
+
# Process dataset for segmentation task
|
| 63 |
+
planner = MedVision_BenchmarkPlannerSegmentation(
|
| 64 |
+
dataset_dir=dataset_dir,
|
| 65 |
+
bm_plan=benchmark_plan,
|
| 66 |
+
dataset_name=dataset_name,
|
| 67 |
+
seed=random_seed,
|
| 68 |
+
split_ratio=split_ratio,
|
| 69 |
+
force_uint16_mask=force_uint16_mask,
|
| 70 |
+
reorient2RAS=reorient2RAS,
|
| 71 |
+
num_proc=_get_cgroup_limited_cpus(),
|
| 72 |
+
)
|
| 73 |
+
planner.process()
|
| 74 |
+
|
| 75 |
+
|
| 76 |
+
if __name__ == "__main__":
|
| 77 |
+
# Set up argument parser
|
| 78 |
+
parser = argparse.ArgumentParser(
|
| 79 |
+
description="Generate benchmark planner for segmentation task."
|
| 80 |
+
)
|
| 81 |
+
parser.add_argument(
|
| 82 |
+
"-d",
|
| 83 |
+
"--dir_datasets_data",
|
| 84 |
+
type=str,
|
| 85 |
+
help="Directory path where datasets will be stored",
|
| 86 |
+
required=True,
|
| 87 |
+
)
|
| 88 |
+
parser.add_argument(
|
| 89 |
+
"-n",
|
| 90 |
+
"--dataset_name",
|
| 91 |
+
type=str,
|
| 92 |
+
help="Name of the dataset",
|
| 93 |
+
required=True,
|
| 94 |
+
)
|
| 95 |
+
parser.add_argument(
|
| 96 |
+
"--random_seed",
|
| 97 |
+
type=int,
|
| 98 |
+
default=1024,
|
| 99 |
+
help="Random seed for reproducibility",
|
| 100 |
+
)
|
| 101 |
+
parser.add_argument(
|
| 102 |
+
"--split_ratio",
|
| 103 |
+
type=float,
|
| 104 |
+
default=0.7,
|
| 105 |
+
help="Train/test split ratio (0-1)",
|
| 106 |
+
)
|
| 107 |
+
parser.add_argument(
|
| 108 |
+
"--force_uint16_mask",
|
| 109 |
+
action="store_true",
|
| 110 |
+
help="Force mask to be uint16",
|
| 111 |
+
)
|
| 112 |
+
parser.add_argument(
|
| 113 |
+
"--reorient2RAS",
|
| 114 |
+
action="store_true",
|
| 115 |
+
help="Reorient images and masks to RAS orientation",
|
| 116 |
+
)
|
| 117 |
+
|
| 118 |
+
args = parser.parse_args()
|
| 119 |
+
|
| 120 |
+
main(
|
| 121 |
+
benchmark_plan=benchmark_plan, # global variable
|
| 122 |
+
dir_datasets_data=args.dir_datasets_data,
|
| 123 |
+
dataset_name=args.dataset_name,
|
| 124 |
+
random_seed=args.random_seed,
|
| 125 |
+
split_ratio=args.split_ratio,
|
| 126 |
+
force_uint16_mask=args.force_uint16_mask,
|
| 127 |
+
reorient2RAS=args.reorient2RAS,
|
| 128 |
+
)
|
src/medvision_ds/datasets/AMOS22/__init__.py
ADDED
|
File without changes
|
src/medvision_ds/datasets/AMOS22/download.py
ADDED
|
@@ -0,0 +1,130 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import shutil
|
| 3 |
+
import argparse
|
| 4 |
+
import glob
|
| 5 |
+
import zipfile
|
| 6 |
+
import urllib.request
|
| 7 |
+
from medvision_ds.utils.preprocess_utils import move_folder
|
| 8 |
+
|
| 9 |
+
# ====================================
|
| 10 |
+
# Dataset Info [!]
|
| 11 |
+
# ====================================
|
| 12 |
+
# Dataset: AMOS22
|
| 13 |
+
# Challenge: https://amos22.grand-challenge.org
|
| 14 |
+
# Data: https://zenodo.org/records/7262581
|
| 15 |
+
# Format: nii.gz
|
| 16 |
+
# ====================================
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
def download_and_extract(dataset_dir, dataset_name, **kwargs):
|
| 20 |
+
"""
|
| 21 |
+
Download and extract the AbdomenAtlas dataset.
|
| 22 |
+
|
| 23 |
+
NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name
|
| 24 |
+
the other arguments must be kwargs
|
| 25 |
+
"""
|
| 26 |
+
# Download files
|
| 27 |
+
current_dir = os.getcwd()
|
| 28 |
+
os.chdir(dataset_dir)
|
| 29 |
+
tmp_dir = os.path.join(dataset_dir, "tmp")
|
| 30 |
+
os.makedirs(tmp_dir, exist_ok=True)
|
| 31 |
+
os.chdir(tmp_dir)
|
| 32 |
+
print(f"Downloading {dataset_name} dataset to {dataset_dir}...")
|
| 33 |
+
|
| 34 |
+
# ====================================
|
| 35 |
+
# Add download logic here [!]
|
| 36 |
+
# ====================================
|
| 37 |
+
# Download dataset
|
| 38 |
+
print("Downloading amos22.zip...")
|
| 39 |
+
urllib.request.urlretrieve(
|
| 40 |
+
"https://zenodo.org/records/7155725/files/amos22.zip?download=1", "amos22.zip"
|
| 41 |
+
)
|
| 42 |
+
|
| 43 |
+
# Extract zip
|
| 44 |
+
print("Extracting amos22.zip...")
|
| 45 |
+
with zipfile.ZipFile("amos22.zip", "r") as zip_ref:
|
| 46 |
+
zip_ref.extractall()
|
| 47 |
+
|
| 48 |
+
# Move contents
|
| 49 |
+
for item in os.listdir("amos22"):
|
| 50 |
+
shutil.move(os.path.join("amos22", item), ".")
|
| 51 |
+
|
| 52 |
+
# Create directories
|
| 53 |
+
for modality in ["CT", "MRI"]:
|
| 54 |
+
for subdir in ["Images", "Masks"]:
|
| 55 |
+
os.makedirs(os.path.join(f"AMOS22-{modality}", subdir), exist_ok=True)
|
| 56 |
+
|
| 57 |
+
# Move image files
|
| 58 |
+
for folder in ["imagesTr", "imagesVa"]:
|
| 59 |
+
if os.path.exists(folder):
|
| 60 |
+
for f in glob.glob(os.path.join(folder, "amos_????.nii.gz")):
|
| 61 |
+
# Extract the number from filename
|
| 62 |
+
num = int(os.path.basename(f)[5:9])
|
| 63 |
+
if num < 507:
|
| 64 |
+
shutil.move(f, os.path.join("AMOS22-CT", "Images"))
|
| 65 |
+
else:
|
| 66 |
+
shutil.move(f, os.path.join("AMOS22-MRI", "Images"))
|
| 67 |
+
|
| 68 |
+
# Move mask files
|
| 69 |
+
for folder in ["labelsTr", "labelsVa"]:
|
| 70 |
+
if os.path.exists(folder):
|
| 71 |
+
for f in glob.glob(os.path.join(folder, "amos_????.nii.gz")):
|
| 72 |
+
# Extract the number from filename
|
| 73 |
+
num = int(os.path.basename(f)[5:9])
|
| 74 |
+
if num < 507:
|
| 75 |
+
shutil.move(f, os.path.join("AMOS22-CT", "Masks"))
|
| 76 |
+
else:
|
| 77 |
+
shutil.move(f, os.path.join("AMOS22-MRI", "Masks"))
|
| 78 |
+
|
| 79 |
+
# Move folder to dataset_dir
|
| 80 |
+
folders_to_move = [
|
| 81 |
+
"AMOS22-CT",
|
| 82 |
+
"AMOS22-MRI",
|
| 83 |
+
]
|
| 84 |
+
for folder in folders_to_move:
|
| 85 |
+
move_folder(
|
| 86 |
+
os.path.join(tmp_dir, folder),
|
| 87 |
+
os.path.join(dataset_dir, folder),
|
| 88 |
+
create_dest=True,
|
| 89 |
+
)
|
| 90 |
+
# ====================================
|
| 91 |
+
|
| 92 |
+
print(f"Download and extraction completed for {dataset_name}")
|
| 93 |
+
os.chdir(dataset_dir)
|
| 94 |
+
shutil.rmtree(tmp_dir)
|
| 95 |
+
os.chdir(current_dir)
|
| 96 |
+
|
| 97 |
+
|
| 98 |
+
def main(dir_datasets_data, dataset_name, **kwargs):
|
| 99 |
+
# Create dataset directory
|
| 100 |
+
dataset_dir = os.path.join(dir_datasets_data, dataset_name)
|
| 101 |
+
os.makedirs(dataset_dir, exist_ok=True)
|
| 102 |
+
|
| 103 |
+
# Change to dataset directory
|
| 104 |
+
os.chdir(dataset_dir)
|
| 105 |
+
|
| 106 |
+
# Download and extract dataset
|
| 107 |
+
download_and_extract(dataset_dir, dataset_name, **kwargs)
|
| 108 |
+
|
| 109 |
+
|
| 110 |
+
if __name__ == "__main__":
|
| 111 |
+
# Set up argument parser
|
| 112 |
+
parser = argparse.ArgumentParser(description="Download and extract dataset")
|
| 113 |
+
parser.add_argument(
|
| 114 |
+
"-d",
|
| 115 |
+
"--dir_datasets_data",
|
| 116 |
+
help="Directory path where datasets will be stored",
|
| 117 |
+
required=True,
|
| 118 |
+
)
|
| 119 |
+
parser.add_argument(
|
| 120 |
+
"-n",
|
| 121 |
+
"--dataset_name",
|
| 122 |
+
help="Name of the dataset",
|
| 123 |
+
required=True,
|
| 124 |
+
)
|
| 125 |
+
args = parser.parse_args()
|
| 126 |
+
|
| 127 |
+
main(
|
| 128 |
+
dir_datasets_data=args.dir_datasets_data,
|
| 129 |
+
dataset_name=args.dataset_name,
|
| 130 |
+
)
|
src/medvision_ds/datasets/AMOS22/preprocess_detection.py
ADDED
|
@@ -0,0 +1,152 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import argparse
|
| 3 |
+
from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus
|
| 4 |
+
from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
# ====================================
|
| 8 |
+
# Dataset Info [!]
|
| 9 |
+
# Do not change keys in
|
| 10 |
+
# - benchmark_plan
|
| 11 |
+
# ====================================
|
| 12 |
+
dataset_info = {
|
| 13 |
+
"dataset": "AMOS22",
|
| 14 |
+
"dataset_website": "https://amos22.grand-challenge.org",
|
| 15 |
+
"dataset_data": [
|
| 16 |
+
"https://zenodo.org/records/7262581",
|
| 17 |
+
],
|
| 18 |
+
"license": ["CC BY 4.0"],
|
| 19 |
+
"paper": ["https://doi.org/10.48550/arXiv.2206.08023"],
|
| 20 |
+
}
|
| 21 |
+
|
| 22 |
+
labels_map = {
|
| 23 |
+
"1": "spleen",
|
| 24 |
+
"2": "right kidney",
|
| 25 |
+
"3": "left kidney",
|
| 26 |
+
"4": "gall bladder",
|
| 27 |
+
"5": "esophagus",
|
| 28 |
+
"6": "liver",
|
| 29 |
+
"7": "stomach",
|
| 30 |
+
"8": "arota",
|
| 31 |
+
"9": "postcava",
|
| 32 |
+
"10": "pancreas",
|
| 33 |
+
"11": "right adrenal gland",
|
| 34 |
+
"12": "left adrenal gland",
|
| 35 |
+
"13": "duodenum",
|
| 36 |
+
"14": "bladder",
|
| 37 |
+
"15": "prostate/uterus",
|
| 38 |
+
}
|
| 39 |
+
|
| 40 |
+
benchmark_plan = {
|
| 41 |
+
"dataset_info": dataset_info,
|
| 42 |
+
"tasks": [
|
| 43 |
+
{
|
| 44 |
+
"image_modality": "CT",
|
| 45 |
+
"image_description": "abdominal computed tomography (CT) scan",
|
| 46 |
+
"image_folder": "AMOS22-CT/Images",
|
| 47 |
+
"mask_folder": "AMOS22-CT/Masks",
|
| 48 |
+
"image_prefix": "",
|
| 49 |
+
"image_suffix": ".nii.gz",
|
| 50 |
+
"mask_prefix": "",
|
| 51 |
+
"mask_suffix": ".nii.gz",
|
| 52 |
+
"labels_map": labels_map,
|
| 53 |
+
},
|
| 54 |
+
{
|
| 55 |
+
"image_modality": "MRI",
|
| 56 |
+
"image_description": "abdominal magnetic resonance imaging (MRI) scan",
|
| 57 |
+
"image_folder": "AMOS22-MRI/Images",
|
| 58 |
+
"mask_folder": "AMOS22-MRI/Masks",
|
| 59 |
+
"image_prefix": "",
|
| 60 |
+
"image_suffix": ".nii.gz",
|
| 61 |
+
"mask_prefix": "",
|
| 62 |
+
"mask_suffix": ".nii.gz",
|
| 63 |
+
"labels_map": labels_map,
|
| 64 |
+
},
|
| 65 |
+
],
|
| 66 |
+
}
|
| 67 |
+
# ====================================
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
def main(
|
| 71 |
+
dir_datasets_data,
|
| 72 |
+
dataset_name,
|
| 73 |
+
benchmark_plan=benchmark_plan,
|
| 74 |
+
random_seed=1024,
|
| 75 |
+
split_ratio=0.7,
|
| 76 |
+
force_uint16_mask=False,
|
| 77 |
+
reorient2RAS=False,
|
| 78 |
+
):
|
| 79 |
+
# Create dataset directory
|
| 80 |
+
dataset_dir = os.path.join(dir_datasets_data, dataset_name)
|
| 81 |
+
os.makedirs(dataset_dir, exist_ok=True)
|
| 82 |
+
|
| 83 |
+
# Change to dataset directory
|
| 84 |
+
os.chdir(dataset_dir)
|
| 85 |
+
|
| 86 |
+
# Process dataset for detection task
|
| 87 |
+
planner = MedVision_BenchmarkPlannerDetection(
|
| 88 |
+
dataset_dir=dataset_dir,
|
| 89 |
+
bm_plan=benchmark_plan,
|
| 90 |
+
dataset_name=dataset_name,
|
| 91 |
+
seed=random_seed,
|
| 92 |
+
split_ratio=split_ratio,
|
| 93 |
+
force_uint16_mask=force_uint16_mask,
|
| 94 |
+
reorient2RAS=reorient2RAS,
|
| 95 |
+
num_proc=_get_cgroup_limited_cpus(),
|
| 96 |
+
)
|
| 97 |
+
planner.process()
|
| 98 |
+
|
| 99 |
+
|
| 100 |
+
if __name__ == "__main__":
|
| 101 |
+
# Set up argument parser
|
| 102 |
+
parser = argparse.ArgumentParser(
|
| 103 |
+
description="Generate benchmark planner for detection task."
|
| 104 |
+
)
|
| 105 |
+
parser.add_argument(
|
| 106 |
+
"-d",
|
| 107 |
+
"--dir_datasets_data",
|
| 108 |
+
type=str,
|
| 109 |
+
help="Directory path where datasets will be stored",
|
| 110 |
+
required=True,
|
| 111 |
+
)
|
| 112 |
+
parser.add_argument(
|
| 113 |
+
"-n",
|
| 114 |
+
"--dataset_name",
|
| 115 |
+
type=str,
|
| 116 |
+
help="Name of the dataset",
|
| 117 |
+
required=True,
|
| 118 |
+
)
|
| 119 |
+
parser.add_argument(
|
| 120 |
+
"--random_seed",
|
| 121 |
+
type=int,
|
| 122 |
+
default=1024,
|
| 123 |
+
help="Random seed for reproducibility",
|
| 124 |
+
)
|
| 125 |
+
parser.add_argument(
|
| 126 |
+
"--split_ratio",
|
| 127 |
+
type=float,
|
| 128 |
+
default=0.7,
|
| 129 |
+
help="Train/test split ratio (0-1)",
|
| 130 |
+
)
|
| 131 |
+
parser.add_argument(
|
| 132 |
+
"--force_uint16_mask",
|
| 133 |
+
action="store_true",
|
| 134 |
+
help="Force mask to be uint16",
|
| 135 |
+
)
|
| 136 |
+
parser.add_argument(
|
| 137 |
+
"--reorient2RAS",
|
| 138 |
+
action="store_true",
|
| 139 |
+
help="Reorient images and masks to RAS orientation",
|
| 140 |
+
)
|
| 141 |
+
|
| 142 |
+
args = parser.parse_args()
|
| 143 |
+
|
| 144 |
+
main(
|
| 145 |
+
benchmark_plan=benchmark_plan, # global variable
|
| 146 |
+
dir_datasets_data=args.dir_datasets_data,
|
| 147 |
+
dataset_name=args.dataset_name,
|
| 148 |
+
random_seed=args.random_seed,
|
| 149 |
+
split_ratio=args.split_ratio,
|
| 150 |
+
force_uint16_mask=args.force_uint16_mask,
|
| 151 |
+
reorient2RAS=args.reorient2RAS,
|
| 152 |
+
)
|
src/medvision_ds/datasets/AMOS22/preprocess_segmentation.py
ADDED
|
@@ -0,0 +1,152 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import argparse
|
| 3 |
+
from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus
|
| 4 |
+
from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
# ====================================
|
| 8 |
+
# Dataset Info [!]
|
| 9 |
+
# Do not change keys in
|
| 10 |
+
# - benchmark_plan
|
| 11 |
+
# ====================================
|
| 12 |
+
dataset_info = {
|
| 13 |
+
"dataset": "AMOS22",
|
| 14 |
+
"dataset_website": "https://amos22.grand-challenge.org",
|
| 15 |
+
"dataset_data": [
|
| 16 |
+
"https://zenodo.org/records/7262581",
|
| 17 |
+
],
|
| 18 |
+
"license": ["CC BY 4.0"],
|
| 19 |
+
"paper": ["https://doi.org/10.48550/arXiv.2206.08023"],
|
| 20 |
+
}
|
| 21 |
+
|
| 22 |
+
labels_map = {
|
| 23 |
+
"1": "spleen",
|
| 24 |
+
"2": "right kidney",
|
| 25 |
+
"3": "left kidney",
|
| 26 |
+
"4": "gall bladder",
|
| 27 |
+
"5": "esophagus",
|
| 28 |
+
"6": "liver",
|
| 29 |
+
"7": "stomach",
|
| 30 |
+
"8": "aorta",
|
| 31 |
+
"9": "postcava",
|
| 32 |
+
"10": "pancreas",
|
| 33 |
+
"11": "right adrenal gland",
|
| 34 |
+
"12": "left adrenal gland",
|
| 35 |
+
"13": "duodenum",
|
| 36 |
+
"14": "bladder",
|
| 37 |
+
"15": "prostate/uterus",
|
| 38 |
+
}
|
| 39 |
+
|
| 40 |
+
benchmark_plan = {
|
| 41 |
+
"dataset_info": dataset_info,
|
| 42 |
+
"tasks": [
|
| 43 |
+
{
|
| 44 |
+
"image_modality": "CT",
|
| 45 |
+
"image_description": "abdominal computed tomography (CT) scan",
|
| 46 |
+
"image_folder": "AMOS22-CT/Images",
|
| 47 |
+
"mask_folder": "AMOS22-CT/Masks",
|
| 48 |
+
"image_prefix": "",
|
| 49 |
+
"image_suffix": ".nii.gz",
|
| 50 |
+
"mask_prefix": "",
|
| 51 |
+
"mask_suffix": ".nii.gz",
|
| 52 |
+
"labels_map": labels_map,
|
| 53 |
+
},
|
| 54 |
+
{
|
| 55 |
+
"image_modality": "MRI",
|
| 56 |
+
"image_description": "abdominal magnetic resonance imaging (MRI) scan",
|
| 57 |
+
"image_folder": "AMOS22-MRI/Images",
|
| 58 |
+
"mask_folder": "AMOS22-MRI/Masks",
|
| 59 |
+
"image_prefix": "",
|
| 60 |
+
"image_suffix": ".nii.gz",
|
| 61 |
+
"mask_prefix": "",
|
| 62 |
+
"mask_suffix": ".nii.gz",
|
| 63 |
+
"labels_map": labels_map,
|
| 64 |
+
},
|
| 65 |
+
],
|
| 66 |
+
}
|
| 67 |
+
# ====================================
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
def main(
|
| 71 |
+
dir_datasets_data,
|
| 72 |
+
dataset_name,
|
| 73 |
+
benchmark_plan=benchmark_plan,
|
| 74 |
+
random_seed=1024,
|
| 75 |
+
split_ratio=0.7,
|
| 76 |
+
force_uint16_mask=False,
|
| 77 |
+
reorient2RAS=False,
|
| 78 |
+
):
|
| 79 |
+
# Create dataset directory
|
| 80 |
+
dataset_dir = os.path.join(dir_datasets_data, dataset_name)
|
| 81 |
+
os.makedirs(dataset_dir, exist_ok=True)
|
| 82 |
+
|
| 83 |
+
# Change to dataset directory
|
| 84 |
+
os.chdir(dataset_dir)
|
| 85 |
+
|
| 86 |
+
# Process dataset for segmentation task
|
| 87 |
+
planner = MedVision_BenchmarkPlannerSegmentation(
|
| 88 |
+
dataset_dir=dataset_dir,
|
| 89 |
+
bm_plan=benchmark_plan,
|
| 90 |
+
dataset_name=dataset_name,
|
| 91 |
+
seed=random_seed,
|
| 92 |
+
split_ratio=split_ratio,
|
| 93 |
+
force_uint16_mask=force_uint16_mask,
|
| 94 |
+
reorient2RAS=reorient2RAS,
|
| 95 |
+
num_proc=_get_cgroup_limited_cpus(),
|
| 96 |
+
)
|
| 97 |
+
planner.process()
|
| 98 |
+
|
| 99 |
+
|
| 100 |
+
if __name__ == "__main__":
|
| 101 |
+
# Set up argument parser
|
| 102 |
+
parser = argparse.ArgumentParser(
|
| 103 |
+
description="Generate benchmark planner for segmentation task."
|
| 104 |
+
)
|
| 105 |
+
parser.add_argument(
|
| 106 |
+
"-d",
|
| 107 |
+
"--dir_datasets_data",
|
| 108 |
+
type=str,
|
| 109 |
+
help="Directory path where datasets will be stored",
|
| 110 |
+
required=True,
|
| 111 |
+
)
|
| 112 |
+
parser.add_argument(
|
| 113 |
+
"-n",
|
| 114 |
+
"--dataset_name",
|
| 115 |
+
type=str,
|
| 116 |
+
help="Name of the dataset",
|
| 117 |
+
required=True,
|
| 118 |
+
)
|
| 119 |
+
parser.add_argument(
|
| 120 |
+
"--random_seed",
|
| 121 |
+
type=int,
|
| 122 |
+
default=1024,
|
| 123 |
+
help="Random seed for reproducibility",
|
| 124 |
+
)
|
| 125 |
+
parser.add_argument(
|
| 126 |
+
"--split_ratio",
|
| 127 |
+
type=float,
|
| 128 |
+
default=0.7,
|
| 129 |
+
help="Train/test split ratio (0-1)",
|
| 130 |
+
)
|
| 131 |
+
parser.add_argument(
|
| 132 |
+
"--force_uint16_mask",
|
| 133 |
+
action="store_true",
|
| 134 |
+
help="Force mask to be uint16",
|
| 135 |
+
)
|
| 136 |
+
parser.add_argument(
|
| 137 |
+
"--reorient2RAS",
|
| 138 |
+
action="store_true",
|
| 139 |
+
help="Reorient images and masks to RAS orientation",
|
| 140 |
+
)
|
| 141 |
+
|
| 142 |
+
args = parser.parse_args()
|
| 143 |
+
|
| 144 |
+
main(
|
| 145 |
+
benchmark_plan=benchmark_plan, # global variable
|
| 146 |
+
dir_datasets_data=args.dir_datasets_data,
|
| 147 |
+
dataset_name=args.dataset_name,
|
| 148 |
+
random_seed=args.random_seed,
|
| 149 |
+
split_ratio=args.split_ratio,
|
| 150 |
+
force_uint16_mask=args.force_uint16_mask,
|
| 151 |
+
reorient2RAS=args.reorient2RAS,
|
| 152 |
+
)
|
src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/__init__.py
ADDED
|
File without changes
|
src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/download_raw.py
ADDED
|
@@ -0,0 +1,161 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import shutil
|
| 3 |
+
import argparse
|
| 4 |
+
import glob
|
| 5 |
+
import nibabel as nib
|
| 6 |
+
from tqdm import tqdm
|
| 7 |
+
from huggingface_hub import snapshot_download
|
| 8 |
+
from medvision_ds.utils.preprocess_utils import move_folder, _get_cgroup_limited_cpus
|
| 9 |
+
from medvision_ds.utils.data_conversion import convert_mask_to_uint16_per_dir, copy_img_header_to_mask
|
| 10 |
+
from medvision_ds.datasets.AbdomenAtlas__1_0__Mini.preprocess_segmentation import (
|
| 11 |
+
benchmark_plan as AbdomenAtlas1_0_Mini_benchmark_plan,
|
| 12 |
+
)
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
# ====================================
|
| 16 |
+
# Dataset Info [!]
|
| 17 |
+
# ====================================
|
| 18 |
+
# Dataset: AbdomenAtlas1.0Mini
|
| 19 |
+
# Website: https://github.com/MrGiovanni/AbdomenAtlas
|
| 20 |
+
# Data: https://huggingface.co/datasets/AbdomenAtlas/AbdomenAtlas1.0Mini
|
| 21 |
+
# Format: nii.gz
|
| 22 |
+
# ====================================
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
def convert_masks_to_uint16(dataset_dir):
|
| 26 |
+
mask_folders = _get_mask_folders(AbdomenAtlas1_0_Mini_benchmark_plan)
|
| 27 |
+
for folder in mask_folders:
|
| 28 |
+
mask_folder = os.path.join(dataset_dir, folder)
|
| 29 |
+
available_cpus = _get_cgroup_limited_cpus()
|
| 30 |
+
convert_mask_to_uint16_per_dir(mask_folder, workers_limit=available_cpus)
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
def wrapper_copy_img_header_to_mask(img_files, mask_dir):
|
| 35 |
+
available_cpus = _get_cgroup_limited_cpus()
|
| 36 |
+
copy_img_header_to_mask(img_files, mask_dir, workers_limit=available_cpus)
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
def _get_mask_folders(bm_plan):
|
| 40 |
+
"""Get unique mask folders from tasks"""
|
| 41 |
+
mask_folders = []
|
| 42 |
+
for task in bm_plan["tasks"]:
|
| 43 |
+
mask_folders.append(task["mask_folder"])
|
| 44 |
+
return list(set(mask_folders))
|
| 45 |
+
|
| 46 |
+
|
| 47 |
+
def download_and_extract(dataset_dir, dataset_name, **kwargs):
|
| 48 |
+
"""
|
| 49 |
+
Download and extract the AbdomenAtlas dataset.
|
| 50 |
+
|
| 51 |
+
NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name
|
| 52 |
+
the other arguments must be kwargs
|
| 53 |
+
"""
|
| 54 |
+
# Download files
|
| 55 |
+
current_dir = os.getcwd()
|
| 56 |
+
os.chdir(dataset_dir)
|
| 57 |
+
tmp_dir = os.path.join(dataset_dir, "tmp")
|
| 58 |
+
os.makedirs(tmp_dir, exist_ok=True)
|
| 59 |
+
os.chdir(tmp_dir)
|
| 60 |
+
print(f"Downloading {dataset_name} dataset to {dataset_dir}...")
|
| 61 |
+
|
| 62 |
+
# ====================================
|
| 63 |
+
# Add download logic here [!]
|
| 64 |
+
# ====================================
|
| 65 |
+
# Download dataset
|
| 66 |
+
dest_dir = "Images-raw"
|
| 67 |
+
snapshot_download(
|
| 68 |
+
repo_id="AbdomenAtlas/AbdomenAtlas1.0Mini",
|
| 69 |
+
repo_type="dataset",
|
| 70 |
+
local_dir=dest_dir,
|
| 71 |
+
revision="4dff62f03f7e4f17cd8c62617bc75fde9893a1e9", # commit hash on 2025-02-20
|
| 72 |
+
max_workers=kwargs.get('max_workers', 1),
|
| 73 |
+
)
|
| 74 |
+
|
| 75 |
+
# Create Images and Masks directories
|
| 76 |
+
os.makedirs("Images", exist_ok=True)
|
| 77 |
+
os.makedirs("Masks", exist_ok=True)
|
| 78 |
+
|
| 79 |
+
# Process each case folder
|
| 80 |
+
for case_dir in glob.glob(os.path.join(dest_dir, "BDMAP*")):
|
| 81 |
+
if os.path.isdir(case_dir):
|
| 82 |
+
case_name = os.path.basename(case_dir)
|
| 83 |
+
# Move CT images
|
| 84 |
+
ct_src = os.path.join(case_dir, "ct.nii.gz")
|
| 85 |
+
if os.path.exists(ct_src):
|
| 86 |
+
shutil.move(ct_src, os.path.join("Images", f"{case_name}.nii.gz"))
|
| 87 |
+
# Move mask files
|
| 88 |
+
mask_src = os.path.join(case_dir, "combined_labels.nii.gz")
|
| 89 |
+
if os.path.exists(mask_src):
|
| 90 |
+
shutil.move(mask_src, os.path.join("Masks", f"{case_name}.nii.gz"))
|
| 91 |
+
|
| 92 |
+
# Copy Nifti header of images to masks (Multiprocessing)
|
| 93 |
+
print("Copying Nifti headers from images to masks...")
|
| 94 |
+
img_files = list(glob.glob(os.path.join("Images", "*.nii.gz")))
|
| 95 |
+
wrapper_copy_img_header_to_mask(img_files, "Masks")
|
| 96 |
+
|
| 97 |
+
# Convert masks to uint16 (Multiprocessing)
|
| 98 |
+
convert_masks_to_uint16(tmp_dir)
|
| 99 |
+
|
| 100 |
+
# Move folder to dataset_dir
|
| 101 |
+
folders_to_move = [
|
| 102 |
+
"Images",
|
| 103 |
+
"Masks",
|
| 104 |
+
]
|
| 105 |
+
for folder in folders_to_move:
|
| 106 |
+
move_folder(
|
| 107 |
+
os.path.join(tmp_dir, folder),
|
| 108 |
+
os.path.join(dataset_dir, folder),
|
| 109 |
+
create_dest=True,
|
| 110 |
+
)
|
| 111 |
+
# ====================================
|
| 112 |
+
|
| 113 |
+
print(f"Download and extraction completed for {dataset_name}")
|
| 114 |
+
os.chdir(dataset_dir)
|
| 115 |
+
shutil.rmtree(tmp_dir)
|
| 116 |
+
os.chdir(current_dir)
|
| 117 |
+
|
| 118 |
+
|
| 119 |
+
def main(dir_datasets_data, dataset_name, **kwargs):
|
| 120 |
+
# Create dataset directory
|
| 121 |
+
dataset_dir = os.path.join(dir_datasets_data, dataset_name)
|
| 122 |
+
os.makedirs(dataset_dir, exist_ok=True)
|
| 123 |
+
|
| 124 |
+
# Change to dataset directory
|
| 125 |
+
os.chdir(dataset_dir)
|
| 126 |
+
|
| 127 |
+
# Download and extract dataset
|
| 128 |
+
download_and_extract(dataset_dir, dataset_name, **kwargs)
|
| 129 |
+
|
| 130 |
+
|
| 131 |
+
if __name__ == "__main__":
|
| 132 |
+
# Set up argument parser
|
| 133 |
+
parser = argparse.ArgumentParser(description="Download and extract dataset")
|
| 134 |
+
parser.add_argument(
|
| 135 |
+
"-d",
|
| 136 |
+
"--dir_datasets_data",
|
| 137 |
+
help="Directory path where datasets will be stored",
|
| 138 |
+
required=True,
|
| 139 |
+
)
|
| 140 |
+
parser.add_argument(
|
| 141 |
+
"-n",
|
| 142 |
+
"--dataset_name",
|
| 143 |
+
help="Name of the dataset",
|
| 144 |
+
required=True,
|
| 145 |
+
)
|
| 146 |
+
parser.add_argument(
|
| 147 |
+
"--max_workers",
|
| 148 |
+
type=int,
|
| 149 |
+
default=1,
|
| 150 |
+
help="Maximum number of workers for download",
|
| 151 |
+
)
|
| 152 |
+
args = parser.parse_args()
|
| 153 |
+
|
| 154 |
+
# Extract known arguments and pass the rest as kwargs
|
| 155 |
+
kwargs = {"max_workers": args.max_workers}
|
| 156 |
+
|
| 157 |
+
main(
|
| 158 |
+
dir_datasets_data=args.dir_datasets_data,
|
| 159 |
+
dataset_name=args.dataset_name,
|
| 160 |
+
**kwargs
|
| 161 |
+
)
|
src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/preprocess_detection.py
ADDED
|
@@ -0,0 +1,136 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import argparse
|
| 3 |
+
from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus
|
| 4 |
+
from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
# ====================================
|
| 8 |
+
# Dataset Info [!]
|
| 9 |
+
# Do not change:
|
| 10 |
+
# - keys in benchmark_plan
|
| 11 |
+
# - variable names: dataset_info, labels_map, benchmark_plan
|
| 12 |
+
# ====================================
|
| 13 |
+
dataset_info = {
|
| 14 |
+
"dataset": "AbdomenAtlas1.0Mini",
|
| 15 |
+
"dataset_website": "https://github.com/MrGiovanni/AbdomenAtlas",
|
| 16 |
+
"dataset_data": [
|
| 17 |
+
"https://huggingface.co/datasets/AbdomenAtlas/AbdomenAtlas1.0Mini"
|
| 18 |
+
],
|
| 19 |
+
"license": ["CC BY-NC-SA 4.0"],
|
| 20 |
+
"paper": ["https://doi.org/10.1016/j.media.2024.103285"],
|
| 21 |
+
}
|
| 22 |
+
|
| 23 |
+
labels_map = {
|
| 24 |
+
"1": "aorta",
|
| 25 |
+
"2": "gallbladder",
|
| 26 |
+
"3": "left kidney",
|
| 27 |
+
"4": "right kidney",
|
| 28 |
+
"5": "liver",
|
| 29 |
+
"6": "pancreas",
|
| 30 |
+
"7": "postcava (inferior vena cava)",
|
| 31 |
+
"8": "spleen",
|
| 32 |
+
"9": "stomach",
|
| 33 |
+
}
|
| 34 |
+
|
| 35 |
+
benchmark_plan = {
|
| 36 |
+
"dataset_info": dataset_info,
|
| 37 |
+
"tasks": [
|
| 38 |
+
{
|
| 39 |
+
"image_modality": "CT",
|
| 40 |
+
"image_description": "abdominal CT scan",
|
| 41 |
+
"image_folder": "Images",
|
| 42 |
+
"mask_folder": "Masks",
|
| 43 |
+
"image_prefix": "",
|
| 44 |
+
"image_suffix": ".nii.gz",
|
| 45 |
+
"mask_prefix": "",
|
| 46 |
+
"mask_suffix": ".nii.gz",
|
| 47 |
+
"labels_map": labels_map,
|
| 48 |
+
},
|
| 49 |
+
],
|
| 50 |
+
}
|
| 51 |
+
# ====================================
|
| 52 |
+
|
| 53 |
+
|
| 54 |
+
def main(
|
| 55 |
+
dir_datasets_data,
|
| 56 |
+
dataset_name,
|
| 57 |
+
benchmark_plan=benchmark_plan,
|
| 58 |
+
random_seed=1024,
|
| 59 |
+
split_ratio=0.7,
|
| 60 |
+
force_uint16_mask=False,
|
| 61 |
+
reorient2RAS=False,
|
| 62 |
+
):
|
| 63 |
+
# Create dataset directory
|
| 64 |
+
dataset_dir = os.path.join(dir_datasets_data, dataset_name)
|
| 65 |
+
os.makedirs(dataset_dir, exist_ok=True)
|
| 66 |
+
|
| 67 |
+
# Change to dataset directory
|
| 68 |
+
os.chdir(dataset_dir)
|
| 69 |
+
|
| 70 |
+
# Process dataset for detection task
|
| 71 |
+
planner = MedVision_BenchmarkPlannerDetection(
|
| 72 |
+
dataset_dir=dataset_dir,
|
| 73 |
+
bm_plan=benchmark_plan,
|
| 74 |
+
dataset_name=dataset_name,
|
| 75 |
+
seed=random_seed,
|
| 76 |
+
split_ratio=split_ratio,
|
| 77 |
+
force_uint16_mask=force_uint16_mask,
|
| 78 |
+
reorient2RAS=reorient2RAS,
|
| 79 |
+
num_proc=_get_cgroup_limited_cpus(),
|
| 80 |
+
)
|
| 81 |
+
planner.process()
|
| 82 |
+
|
| 83 |
+
|
| 84 |
+
if __name__ == "__main__":
|
| 85 |
+
# Set up argument parser
|
| 86 |
+
parser = argparse.ArgumentParser(
|
| 87 |
+
description="Generate benchmark planner for detection task."
|
| 88 |
+
)
|
| 89 |
+
parser.add_argument(
|
| 90 |
+
"-d",
|
| 91 |
+
"--dir_datasets_data",
|
| 92 |
+
type=str,
|
| 93 |
+
help="Directory path where datasets will be stored",
|
| 94 |
+
required=True,
|
| 95 |
+
)
|
| 96 |
+
parser.add_argument(
|
| 97 |
+
"-n",
|
| 98 |
+
"--dataset_name",
|
| 99 |
+
type=str,
|
| 100 |
+
help="Name of the dataset",
|
| 101 |
+
required=True,
|
| 102 |
+
)
|
| 103 |
+
parser.add_argument(
|
| 104 |
+
"--random_seed",
|
| 105 |
+
type=int,
|
| 106 |
+
default=1024,
|
| 107 |
+
help="Random seed for reproducibility",
|
| 108 |
+
)
|
| 109 |
+
parser.add_argument(
|
| 110 |
+
"--split_ratio",
|
| 111 |
+
type=float,
|
| 112 |
+
default=0.7,
|
| 113 |
+
help="Train/test split ratio (0-1)",
|
| 114 |
+
)
|
| 115 |
+
parser.add_argument(
|
| 116 |
+
"--force_uint16_mask",
|
| 117 |
+
action="store_true",
|
| 118 |
+
help="Force mask to be uint16",
|
| 119 |
+
)
|
| 120 |
+
parser.add_argument(
|
| 121 |
+
"--reorient2RAS",
|
| 122 |
+
action="store_true",
|
| 123 |
+
help="Reorient images and masks to RAS orientation",
|
| 124 |
+
)
|
| 125 |
+
|
| 126 |
+
args = parser.parse_args()
|
| 127 |
+
|
| 128 |
+
main(
|
| 129 |
+
benchmark_plan=benchmark_plan, # global variable
|
| 130 |
+
dir_datasets_data=args.dir_datasets_data,
|
| 131 |
+
dataset_name=args.dataset_name,
|
| 132 |
+
random_seed=args.random_seed,
|
| 133 |
+
split_ratio=args.split_ratio,
|
| 134 |
+
force_uint16_mask=args.force_uint16_mask,
|
| 135 |
+
reorient2RAS=args.reorient2RAS,
|
| 136 |
+
)
|
src/medvision_ds/datasets/AbdomenAtlas__1_0__Mini/preprocess_segmentation.py
ADDED
|
@@ -0,0 +1,137 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import argparse
|
| 3 |
+
from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus
|
| 4 |
+
from medvision_ds.utils.benchmark_planner import (
|
| 5 |
+
MedVision_BenchmarkPlannerSegmentation,
|
| 6 |
+
)
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
# ====================================
|
| 10 |
+
# Dataset Info [!]
|
| 11 |
+
# Do not change keys in
|
| 12 |
+
# - benchmark_plan
|
| 13 |
+
# ====================================
|
| 14 |
+
dataset_info = {
|
| 15 |
+
"dataset": "AbdomenAtlas1.0Mini",
|
| 16 |
+
"dataset_website": "https://github.com/MrGiovanni/AbdomenAtlas",
|
| 17 |
+
"dataset_data": [
|
| 18 |
+
"https://huggingface.co/datasets/AbdomenAtlas/AbdomenAtlas1.0Mini"
|
| 19 |
+
],
|
| 20 |
+
"license": ["CC BY-NC-SA 4.0"],
|
| 21 |
+
"paper": ["https://doi.org/10.1016/j.media.2024.103285"],
|
| 22 |
+
}
|
| 23 |
+
|
| 24 |
+
labels_map = {
|
| 25 |
+
"1": "aorta",
|
| 26 |
+
"2": "gallbladder",
|
| 27 |
+
"3": "left kidney",
|
| 28 |
+
"4": "right kidney",
|
| 29 |
+
"5": "liver",
|
| 30 |
+
"6": "pancreas",
|
| 31 |
+
"7": "postcava (inferior vena cava)",
|
| 32 |
+
"8": "spleen",
|
| 33 |
+
"9": "stomach",
|
| 34 |
+
}
|
| 35 |
+
|
| 36 |
+
benchmark_plan = {
|
| 37 |
+
"dataset_info": dataset_info,
|
| 38 |
+
"tasks": [
|
| 39 |
+
{
|
| 40 |
+
"image_modality": "CT",
|
| 41 |
+
"image_description": "abdominal CT scan",
|
| 42 |
+
"image_folder": "Images",
|
| 43 |
+
"mask_folder": "Masks",
|
| 44 |
+
"image_prefix": "",
|
| 45 |
+
"image_suffix": ".nii.gz",
|
| 46 |
+
"mask_prefix": "",
|
| 47 |
+
"mask_suffix": ".nii.gz",
|
| 48 |
+
"labels_map": labels_map,
|
| 49 |
+
},
|
| 50 |
+
],
|
| 51 |
+
}
|
| 52 |
+
# ====================================
|
| 53 |
+
|
| 54 |
+
|
| 55 |
+
def main(
|
| 56 |
+
dir_datasets_data,
|
| 57 |
+
dataset_name,
|
| 58 |
+
benchmark_plan=benchmark_plan,
|
| 59 |
+
random_seed=1024,
|
| 60 |
+
split_ratio=0.7,
|
| 61 |
+
force_uint16_mask=False,
|
| 62 |
+
reorient2RAS=False,
|
| 63 |
+
):
|
| 64 |
+
# Create dataset directory
|
| 65 |
+
dataset_dir = os.path.join(dir_datasets_data, dataset_name)
|
| 66 |
+
os.makedirs(dataset_dir, exist_ok=True)
|
| 67 |
+
|
| 68 |
+
# Change to dataset directory
|
| 69 |
+
os.chdir(dataset_dir)
|
| 70 |
+
|
| 71 |
+
# Process dataset for segmentation task
|
| 72 |
+
planner = MedVision_BenchmarkPlannerSegmentation(
|
| 73 |
+
dataset_dir=dataset_dir,
|
| 74 |
+
bm_plan=benchmark_plan,
|
| 75 |
+
dataset_name=dataset_name,
|
| 76 |
+
seed=random_seed,
|
| 77 |
+
split_ratio=split_ratio,
|
| 78 |
+
force_uint16_mask=force_uint16_mask,
|
| 79 |
+
reorient2RAS=reorient2RAS,
|
| 80 |
+
num_proc=_get_cgroup_limited_cpus(),
|
| 81 |
+
)
|
| 82 |
+
planner.process()
|
| 83 |
+
|
| 84 |
+
|
| 85 |
+
if __name__ == "__main__":
|
| 86 |
+
# Set up argument parser
|
| 87 |
+
parser = argparse.ArgumentParser(
|
| 88 |
+
description="Generate benchmark planner for segmentation task."
|
| 89 |
+
)
|
| 90 |
+
parser.add_argument(
|
| 91 |
+
"-d",
|
| 92 |
+
"--dir_datasets_data",
|
| 93 |
+
type=str,
|
| 94 |
+
help="Directory path where datasets will be stored",
|
| 95 |
+
required=True,
|
| 96 |
+
)
|
| 97 |
+
parser.add_argument(
|
| 98 |
+
"-n",
|
| 99 |
+
"--dataset_name",
|
| 100 |
+
type=str,
|
| 101 |
+
help="Name of the dataset",
|
| 102 |
+
required=True,
|
| 103 |
+
)
|
| 104 |
+
parser.add_argument(
|
| 105 |
+
"--random_seed",
|
| 106 |
+
type=int,
|
| 107 |
+
default=1024,
|
| 108 |
+
help="Random seed for reproducibility",
|
| 109 |
+
)
|
| 110 |
+
parser.add_argument(
|
| 111 |
+
"--split_ratio",
|
| 112 |
+
type=float,
|
| 113 |
+
default=0.7,
|
| 114 |
+
help="Train/test split ratio (0-1)",
|
| 115 |
+
)
|
| 116 |
+
parser.add_argument(
|
| 117 |
+
"--force_uint16_mask",
|
| 118 |
+
action="store_true",
|
| 119 |
+
help="Force mask to be uint16",
|
| 120 |
+
)
|
| 121 |
+
parser.add_argument(
|
| 122 |
+
"--reorient2RAS",
|
| 123 |
+
action="store_true",
|
| 124 |
+
help="Reorient images and masks to RAS orientation",
|
| 125 |
+
)
|
| 126 |
+
|
| 127 |
+
args = parser.parse_args()
|
| 128 |
+
|
| 129 |
+
main(
|
| 130 |
+
benchmark_plan=benchmark_plan, # global variable
|
| 131 |
+
dir_datasets_data=args.dir_datasets_data,
|
| 132 |
+
dataset_name=args.dataset_name,
|
| 133 |
+
random_seed=args.random_seed,
|
| 134 |
+
split_ratio=args.split_ratio,
|
| 135 |
+
force_uint16_mask=args.force_uint16_mask,
|
| 136 |
+
reorient2RAS=args.reorient2RAS,
|
| 137 |
+
)
|
src/medvision_ds/datasets/AbdomenCT_1K/__init__.py
ADDED
|
File without changes
|
src/medvision_ds/datasets/AbdomenCT_1K/download.py
ADDED
|
@@ -0,0 +1,127 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import shutil
|
| 3 |
+
import argparse
|
| 4 |
+
import zipfile
|
| 5 |
+
import urllib.request
|
| 6 |
+
import py7zr
|
| 7 |
+
from medvision_ds.utils.preprocess_utils import match_and_clean_files, move_folder
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
# ====================================
|
| 11 |
+
# Dataset Info [!]
|
| 12 |
+
# ====================================
|
| 13 |
+
# Dataset: AbdomenCT-1K
|
| 14 |
+
# Website: https://github.com/JunMa11/AbdomenCT-1K
|
| 15 |
+
# Data: https://zenodo.org/records/5903099; https://zenodo.org/records/5903846; https://zenodo.org/records/5903769
|
| 16 |
+
# Format: nii.gz
|
| 17 |
+
# ====================================
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
def download_and_extract(dataset_dir, dataset_name, **kwargs):
|
| 21 |
+
"""
|
| 22 |
+
Download and extract the AbdomenAtlas dataset.
|
| 23 |
+
|
| 24 |
+
NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name
|
| 25 |
+
the other arguments must be kwargs
|
| 26 |
+
"""
|
| 27 |
+
# Download files
|
| 28 |
+
current_dir = os.getcwd()
|
| 29 |
+
os.chdir(dataset_dir)
|
| 30 |
+
tmp_dir = os.path.join(dataset_dir, "tmp")
|
| 31 |
+
os.makedirs(tmp_dir, exist_ok=True)
|
| 32 |
+
os.chdir(tmp_dir)
|
| 33 |
+
print(f"Downloading {dataset_name} dataset to {dataset_dir}...")
|
| 34 |
+
|
| 35 |
+
# ====================================
|
| 36 |
+
# Add download logic here [!]
|
| 37 |
+
# ====================================
|
| 38 |
+
# Downloading dataset
|
| 39 |
+
urls = [
|
| 40 |
+
"https://zenodo.org/records/5903099/files/AbdomenCT-1K-ImagePart1.zip?download=1",
|
| 41 |
+
"https://zenodo.org/records/5903846/files/AbdomenCT-1K-ImagePart2.zip?download=1",
|
| 42 |
+
"https://zenodo.org/records/5903769/files/AbdomenCT-1K-ImagePart3.zip?download=1",
|
| 43 |
+
"https://zenodo.org/records/5903769/files/Mask.7z?download=1",
|
| 44 |
+
]
|
| 45 |
+
filenames = [
|
| 46 |
+
"AbdomenCT-1K-ImagePart1.zip",
|
| 47 |
+
"AbdomenCT-1K-ImagePart2.zip",
|
| 48 |
+
"AbdomenCT-1K-ImagePart3.zip",
|
| 49 |
+
"Mask.7z",
|
| 50 |
+
]
|
| 51 |
+
# Download files using urllib
|
| 52 |
+
for url, filename in zip(urls, filenames):
|
| 53 |
+
print(f"Downloading {filename}...")
|
| 54 |
+
urllib.request.urlretrieve(url, filename)
|
| 55 |
+
# Extract zip archives
|
| 56 |
+
for filename in filenames[:3]:
|
| 57 |
+
print(f"Extracting {filename}...")
|
| 58 |
+
with zipfile.ZipFile(filename, "r") as zip_ref:
|
| 59 |
+
zip_ref.extractall()
|
| 60 |
+
# Extract 7z archive
|
| 61 |
+
print(f"Extracting Mask.7z...")
|
| 62 |
+
with py7zr.SevenZipFile("Mask.7z", "r") as archive:
|
| 63 |
+
archive.extractall(path="Masks")
|
| 64 |
+
|
| 65 |
+
# Combine data
|
| 66 |
+
for part in ["AbdomenCT-1K-ImagePart2", "AbdomenCT-1K-ImagePart3"]:
|
| 67 |
+
for file in os.listdir(part):
|
| 68 |
+
if file.endswith(".nii.gz"):
|
| 69 |
+
shutil.move(os.path.join(part, file), "AbdomenCT-1K-ImagePart1")
|
| 70 |
+
shutil.rmtree("Images") if os.path.exists("Images") else None
|
| 71 |
+
shutil.move("AbdomenCT-1K-ImagePart1", "Images")
|
| 72 |
+
|
| 73 |
+
# Removing image files without corresponding mask files
|
| 74 |
+
match_and_clean_files("Images", "Masks")
|
| 75 |
+
|
| 76 |
+
# Move folder to dataset_dir
|
| 77 |
+
folders_to_move = [
|
| 78 |
+
"Images",
|
| 79 |
+
"Masks",
|
| 80 |
+
]
|
| 81 |
+
for folder in folders_to_move:
|
| 82 |
+
move_folder(
|
| 83 |
+
os.path.join(tmp_dir, folder),
|
| 84 |
+
os.path.join(dataset_dir, folder),
|
| 85 |
+
create_dest=True,
|
| 86 |
+
)
|
| 87 |
+
# ====================================
|
| 88 |
+
|
| 89 |
+
print(f"Download and extraction completed for {dataset_name}")
|
| 90 |
+
os.chdir(dataset_dir)
|
| 91 |
+
shutil.rmtree(tmp_dir)
|
| 92 |
+
os.chdir(current_dir)
|
| 93 |
+
|
| 94 |
+
|
| 95 |
+
def main(dir_datasets_data, dataset_name, **kwargs):
|
| 96 |
+
# Create dataset directory
|
| 97 |
+
dataset_dir = os.path.join(dir_datasets_data, dataset_name)
|
| 98 |
+
os.makedirs(dataset_dir, exist_ok=True)
|
| 99 |
+
|
| 100 |
+
# Change to dataset directory
|
| 101 |
+
os.chdir(dataset_dir)
|
| 102 |
+
|
| 103 |
+
# Download and extract dataset
|
| 104 |
+
download_and_extract(dataset_dir, dataset_name, **kwargs)
|
| 105 |
+
|
| 106 |
+
|
| 107 |
+
if __name__ == "__main__":
|
| 108 |
+
# Set up argument parser
|
| 109 |
+
parser = argparse.ArgumentParser(description="Download and extract dataset")
|
| 110 |
+
parser.add_argument(
|
| 111 |
+
"-d",
|
| 112 |
+
"--dir_datasets_data",
|
| 113 |
+
help="Directory path where datasets will be stored",
|
| 114 |
+
required=True,
|
| 115 |
+
)
|
| 116 |
+
parser.add_argument(
|
| 117 |
+
"-n",
|
| 118 |
+
"--dataset_name",
|
| 119 |
+
help="Name of the dataset",
|
| 120 |
+
required=True,
|
| 121 |
+
)
|
| 122 |
+
args = parser.parse_args()
|
| 123 |
+
|
| 124 |
+
main(
|
| 125 |
+
dir_datasets_data=args.dir_datasets_data,
|
| 126 |
+
dataset_name=args.dataset_name,
|
| 127 |
+
)
|
src/medvision_ds/datasets/AbdomenCT_1K/preprocess_detection.py
ADDED
|
@@ -0,0 +1,131 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import argparse
|
| 3 |
+
from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus
|
| 4 |
+
from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerDetection
|
| 5 |
+
|
| 6 |
+
# ====================================
|
| 7 |
+
# Dataset Info [!]
|
| 8 |
+
# Do not change keys in
|
| 9 |
+
# - benchmark_plan
|
| 10 |
+
# ====================================
|
| 11 |
+
dataset_info = {
|
| 12 |
+
"dataset": "AbdomenCT-1K",
|
| 13 |
+
"dataset_website": "https://github.com/JunMa11/AbdomenCT-1K",
|
| 14 |
+
"dataset_data": [
|
| 15 |
+
"https://zenodo.org/records/5903099",
|
| 16 |
+
"https://zenodo.org/records/5903846",
|
| 17 |
+
"https://zenodo.org/records/5903769",
|
| 18 |
+
],
|
| 19 |
+
"license": ["CC BY 4.0"],
|
| 20 |
+
"paper": ["https://doi.org/10.1109/TPAMI.2021.3100536"],
|
| 21 |
+
}
|
| 22 |
+
|
| 23 |
+
labels_map = {
|
| 24 |
+
"1": "liver",
|
| 25 |
+
"2": "kidney",
|
| 26 |
+
"3": "spleen",
|
| 27 |
+
"4": "pancreas",
|
| 28 |
+
}
|
| 29 |
+
|
| 30 |
+
benchmark_plan = {
|
| 31 |
+
"dataset_info": dataset_info,
|
| 32 |
+
"tasks": [
|
| 33 |
+
{
|
| 34 |
+
"image_modality": "CT",
|
| 35 |
+
"image_description": "abdominal computed tomography (CT) scan",
|
| 36 |
+
"image_folder": "Images",
|
| 37 |
+
"mask_folder": "Masks",
|
| 38 |
+
"image_prefix": "",
|
| 39 |
+
"image_suffix": "_0000.nii.gz",
|
| 40 |
+
"mask_prefix": "",
|
| 41 |
+
"mask_suffix": ".nii.gz",
|
| 42 |
+
"labels_map": labels_map,
|
| 43 |
+
},
|
| 44 |
+
],
|
| 45 |
+
}
|
| 46 |
+
# ====================================
|
| 47 |
+
|
| 48 |
+
|
| 49 |
+
def main(
|
| 50 |
+
dir_datasets_data,
|
| 51 |
+
dataset_name,
|
| 52 |
+
benchmark_plan=benchmark_plan,
|
| 53 |
+
random_seed=1024,
|
| 54 |
+
split_ratio=0.7,
|
| 55 |
+
force_uint16_mask=False,
|
| 56 |
+
reorient2RAS=False,
|
| 57 |
+
):
|
| 58 |
+
# Create dataset directory
|
| 59 |
+
dataset_dir = os.path.join(dir_datasets_data, dataset_name)
|
| 60 |
+
os.makedirs(dataset_dir, exist_ok=True)
|
| 61 |
+
|
| 62 |
+
# Change to dataset directory
|
| 63 |
+
os.chdir(dataset_dir)
|
| 64 |
+
|
| 65 |
+
# Process dataset for detection task
|
| 66 |
+
planner = MedVision_BenchmarkPlannerDetection(
|
| 67 |
+
dataset_dir=dataset_dir,
|
| 68 |
+
bm_plan=benchmark_plan,
|
| 69 |
+
dataset_name=dataset_name,
|
| 70 |
+
seed=random_seed,
|
| 71 |
+
split_ratio=split_ratio,
|
| 72 |
+
force_uint16_mask=force_uint16_mask,
|
| 73 |
+
reorient2RAS=reorient2RAS,
|
| 74 |
+
num_proc=_get_cgroup_limited_cpus(),
|
| 75 |
+
)
|
| 76 |
+
planner.process()
|
| 77 |
+
|
| 78 |
+
|
| 79 |
+
if __name__ == "__main__":
|
| 80 |
+
# Set up argument parser
|
| 81 |
+
parser = argparse.ArgumentParser(
|
| 82 |
+
description="Generate benchmark planner for detection task."
|
| 83 |
+
)
|
| 84 |
+
parser.add_argument(
|
| 85 |
+
"-d",
|
| 86 |
+
"--dir_datasets_data",
|
| 87 |
+
type=str,
|
| 88 |
+
help="Directory path where datasets will be stored",
|
| 89 |
+
required=True,
|
| 90 |
+
)
|
| 91 |
+
parser.add_argument(
|
| 92 |
+
"-n",
|
| 93 |
+
"--dataset_name",
|
| 94 |
+
type=str,
|
| 95 |
+
help="Name of the dataset",
|
| 96 |
+
required=True,
|
| 97 |
+
)
|
| 98 |
+
parser.add_argument(
|
| 99 |
+
"--random_seed",
|
| 100 |
+
type=int,
|
| 101 |
+
default=1024,
|
| 102 |
+
help="Random seed for reproducibility",
|
| 103 |
+
)
|
| 104 |
+
parser.add_argument(
|
| 105 |
+
"--split_ratio",
|
| 106 |
+
type=float,
|
| 107 |
+
default=0.7,
|
| 108 |
+
help="Train/test split ratio (0-1)",
|
| 109 |
+
)
|
| 110 |
+
parser.add_argument(
|
| 111 |
+
"--force_uint16_mask",
|
| 112 |
+
action="store_true",
|
| 113 |
+
help="Force mask to be uint16",
|
| 114 |
+
)
|
| 115 |
+
parser.add_argument(
|
| 116 |
+
"--reorient2RAS",
|
| 117 |
+
action="store_true",
|
| 118 |
+
help="Reorient images and masks to RAS orientation",
|
| 119 |
+
)
|
| 120 |
+
|
| 121 |
+
args = parser.parse_args()
|
| 122 |
+
|
| 123 |
+
main(
|
| 124 |
+
benchmark_plan=benchmark_plan, # global variable
|
| 125 |
+
dir_datasets_data=args.dir_datasets_data,
|
| 126 |
+
dataset_name=args.dataset_name,
|
| 127 |
+
random_seed=args.random_seed,
|
| 128 |
+
split_ratio=args.split_ratio,
|
| 129 |
+
force_uint16_mask=args.force_uint16_mask,
|
| 130 |
+
reorient2RAS=args.reorient2RAS,
|
| 131 |
+
)
|
src/medvision_ds/datasets/AbdomenCT_1K/preprocess_segmentation.py
ADDED
|
@@ -0,0 +1,131 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import argparse
|
| 3 |
+
from medvision_ds.utils.preprocess_utils import _get_cgroup_limited_cpus
|
| 4 |
+
from medvision_ds.utils.benchmark_planner import MedVision_BenchmarkPlannerSegmentation
|
| 5 |
+
|
| 6 |
+
# ====================================
|
| 7 |
+
# Dataset Info [!]
|
| 8 |
+
# Do not change keys in
|
| 9 |
+
# - benchmark_plan
|
| 10 |
+
# ====================================
|
| 11 |
+
dataset_info = {
|
| 12 |
+
"dataset": "AbdomenCT-1K",
|
| 13 |
+
"dataset_website": "https://github.com/JunMa11/AbdomenCT-1K",
|
| 14 |
+
"dataset_data": [
|
| 15 |
+
"https://zenodo.org/records/5903099",
|
| 16 |
+
"https://zenodo.org/records/5903846",
|
| 17 |
+
"https://zenodo.org/records/5903769",
|
| 18 |
+
],
|
| 19 |
+
"license": ["CC BY 4.0"],
|
| 20 |
+
"paper": ["https://doi.org/10.1109/TPAMI.2021.3100536"],
|
| 21 |
+
}
|
| 22 |
+
|
| 23 |
+
labels_map = {
|
| 24 |
+
"1": "liver",
|
| 25 |
+
"2": "kidney",
|
| 26 |
+
"3": "spleen",
|
| 27 |
+
"4": "pancreas",
|
| 28 |
+
}
|
| 29 |
+
|
| 30 |
+
benchmark_plan = {
|
| 31 |
+
"dataset_info": dataset_info,
|
| 32 |
+
"tasks": [
|
| 33 |
+
{
|
| 34 |
+
"image_modality": "CT",
|
| 35 |
+
"image_description": "abdominal computed tomography (CT) scan",
|
| 36 |
+
"image_folder": "Images",
|
| 37 |
+
"mask_folder": "Masks",
|
| 38 |
+
"image_prefix": "",
|
| 39 |
+
"image_suffix": "_0000.nii.gz",
|
| 40 |
+
"mask_prefix": "",
|
| 41 |
+
"mask_suffix": ".nii.gz",
|
| 42 |
+
"labels_map": labels_map,
|
| 43 |
+
},
|
| 44 |
+
],
|
| 45 |
+
}
|
| 46 |
+
# ====================================
|
| 47 |
+
|
| 48 |
+
|
| 49 |
+
def main(
|
| 50 |
+
dir_datasets_data,
|
| 51 |
+
dataset_name,
|
| 52 |
+
benchmark_plan=benchmark_plan,
|
| 53 |
+
random_seed=1024,
|
| 54 |
+
split_ratio=0.7,
|
| 55 |
+
force_uint16_mask=False,
|
| 56 |
+
reorient2RAS=False,
|
| 57 |
+
):
|
| 58 |
+
# Create dataset directory
|
| 59 |
+
dataset_dir = os.path.join(dir_datasets_data, dataset_name)
|
| 60 |
+
os.makedirs(dataset_dir, exist_ok=True)
|
| 61 |
+
|
| 62 |
+
# Change to dataset directory
|
| 63 |
+
os.chdir(dataset_dir)
|
| 64 |
+
|
| 65 |
+
# Process dataset for segmentation task
|
| 66 |
+
planner = MedVision_BenchmarkPlannerSegmentation(
|
| 67 |
+
dataset_dir=dataset_dir,
|
| 68 |
+
bm_plan=benchmark_plan,
|
| 69 |
+
dataset_name=dataset_name,
|
| 70 |
+
seed=random_seed,
|
| 71 |
+
split_ratio=split_ratio,
|
| 72 |
+
force_uint16_mask=force_uint16_mask,
|
| 73 |
+
reorient2RAS=reorient2RAS,
|
| 74 |
+
num_proc=_get_cgroup_limited_cpus(),
|
| 75 |
+
)
|
| 76 |
+
planner.process()
|
| 77 |
+
|
| 78 |
+
|
| 79 |
+
if __name__ == "__main__":
|
| 80 |
+
# Set up argument parser
|
| 81 |
+
parser = argparse.ArgumentParser(
|
| 82 |
+
description="Generate benchmark planner for segmentation task."
|
| 83 |
+
)
|
| 84 |
+
parser.add_argument(
|
| 85 |
+
"-d",
|
| 86 |
+
"--dir_datasets_data",
|
| 87 |
+
type=str,
|
| 88 |
+
help="Directory path where datasets will be stored",
|
| 89 |
+
required=True,
|
| 90 |
+
)
|
| 91 |
+
parser.add_argument(
|
| 92 |
+
"-n",
|
| 93 |
+
"--dataset_name",
|
| 94 |
+
type=str,
|
| 95 |
+
help="Name of the dataset",
|
| 96 |
+
required=True,
|
| 97 |
+
)
|
| 98 |
+
parser.add_argument(
|
| 99 |
+
"--random_seed",
|
| 100 |
+
type=int,
|
| 101 |
+
default=1024,
|
| 102 |
+
help="Random seed for reproducibility",
|
| 103 |
+
)
|
| 104 |
+
parser.add_argument(
|
| 105 |
+
"--split_ratio",
|
| 106 |
+
type=float,
|
| 107 |
+
default=0.7,
|
| 108 |
+
help="Train/test split ratio (0-1)",
|
| 109 |
+
)
|
| 110 |
+
parser.add_argument(
|
| 111 |
+
"--force_uint16_mask",
|
| 112 |
+
action="store_true",
|
| 113 |
+
help="Force mask to be uint16",
|
| 114 |
+
)
|
| 115 |
+
parser.add_argument(
|
| 116 |
+
"--reorient2RAS",
|
| 117 |
+
action="store_true",
|
| 118 |
+
help="Reorient images and masks to RAS orientation",
|
| 119 |
+
)
|
| 120 |
+
|
| 121 |
+
args = parser.parse_args()
|
| 122 |
+
|
| 123 |
+
main(
|
| 124 |
+
benchmark_plan=benchmark_plan, # global variable
|
| 125 |
+
dir_datasets_data=args.dir_datasets_data,
|
| 126 |
+
dataset_name=args.dataset_name,
|
| 127 |
+
random_seed=args.random_seed,
|
| 128 |
+
split_ratio=args.split_ratio,
|
| 129 |
+
force_uint16_mask=args.force_uint16_mask,
|
| 130 |
+
reorient2RAS=args.reorient2RAS,
|
| 131 |
+
)
|
src/medvision_ds/datasets/BCV15/__init__.py
ADDED
|
File without changes
|
src/medvision_ds/datasets/BCV15/download.py
ADDED
|
@@ -0,0 +1,147 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import shutil
|
| 3 |
+
import synapseclient
|
| 4 |
+
import zipfile
|
| 5 |
+
import argparse
|
| 6 |
+
from medvision_ds.utils.preprocess_utils import move_folder
|
| 7 |
+
|
| 8 |
+
# ====================================
|
| 9 |
+
# Dataset Info [!]
|
| 10 |
+
# ====================================
|
| 11 |
+
# Dataset: BCV15
|
| 12 |
+
# Challenge: https://www.synapse.org/Synapse:syn3193805/wiki/89480
|
| 13 |
+
# Data: Abdomen: https://www.synapse.org/Synapse:syn3193805/wiki/217789;
|
| 14 |
+
# Cervix: https://www.synapse.org/Synapse:syn3193805/wiki/217790
|
| 15 |
+
# Format: nii.gz
|
| 16 |
+
# ====================================
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
def download_and_extract(dataset_dir, dataset_name, **kwargs):
|
| 20 |
+
"""
|
| 21 |
+
Download and extract the AbdomenAtlas dataset.
|
| 22 |
+
|
| 23 |
+
NOTE: Function signature: the first 2 arguments must be dataset_dir and dataset_name
|
| 24 |
+
the other arguments must be kwargs
|
| 25 |
+
"""
|
| 26 |
+
# Download files
|
| 27 |
+
current_dir = os.getcwd()
|
| 28 |
+
os.chdir(dataset_dir)
|
| 29 |
+
tmp_dir = os.path.join(dataset_dir, "tmp")
|
| 30 |
+
os.makedirs(tmp_dir, exist_ok=True)
|
| 31 |
+
os.chdir(tmp_dir)
|
| 32 |
+
print(f"Downloading {dataset_name} dataset to {dataset_dir}...")
|
| 33 |
+
|
| 34 |
+
# ====================================
|
| 35 |
+
# Add download logic here [!]
|
| 36 |
+
# ====================================
|
| 37 |
+
# Initialize Synapse client
|
| 38 |
+
syn = synapseclient.Synapse()
|
| 39 |
+
token = os.environ.get("SYNAPSE_TOKEN")
|
| 40 |
+
if not token:
|
| 41 |
+
raise ValueError("SYNAPSE_TOKEN environment variable not set")
|
| 42 |
+
syn.login(authToken=token)
|
| 43 |
+
|
| 44 |
+
# Download datasets
|
| 45 |
+
syn.get("syn3379050", downloadLocation="Abdomen")
|
| 46 |
+
syn.get("syn3546986", downloadLocation="Cervix")
|
| 47 |
+
|
| 48 |
+
# Extract zip files
|
| 49 |
+
with zipfile.ZipFile(os.path.join("Abdomen", "RawData.zip"), "r") as zip_ref:
|
| 50 |
+
zip_ref.extractall("Abdomen")
|
| 51 |
+
with zipfile.ZipFile(os.path.join("Cervix", "CervixRawData.zip"), "r") as zip_ref:
|
| 52 |
+
zip_ref.extractall("Cervix")
|
| 53 |
+
|
| 54 |
+
# Create directories and move files for Abdomen
|
| 55 |
+
os.makedirs(os.path.join("Abdomen", "Images"), exist_ok=True)
|
| 56 |
+
os.makedirs(os.path.join("Abdomen", "Masks"), exist_ok=True)
|
| 57 |
+
for f in os.listdir(os.path.join("Abdomen", "RawData", "Training", "img")):
|
| 58 |
+
if f.endswith(".nii.gz"):
|
| 59 |
+
shutil.move(
|
| 60 |
+
os.path.join("Abdomen", "RawData", "Training", "img", f),
|
| 61 |
+
os.path.join("Abdomen", "Images", f),
|
| 62 |
+
)
|
| 63 |
+
for f in os.listdir(os.path.join("Abdomen", "RawData", "Training", "label")):
|
| 64 |
+
if f.endswith(".nii.gz"):
|
| 65 |
+
shutil.move(
|
| 66 |
+
os.path.join("Abdomen", "RawData", "Training", "label", f),
|
| 67 |
+
os.path.join("Abdomen", "Masks", f),
|
| 68 |
+
)
|
| 69 |
+
|
| 70 |
+
# Create directories and move files for Cervix
|
| 71 |
+
os.makedirs(os.path.join("Cervix", "Images"), exist_ok=True)
|
| 72 |
+
os.makedirs(os.path.join("Cervix", "Masks"), exist_ok=True)
|
| 73 |
+
for f in os.listdir(os.path.join("Cervix", "RawData", "Training", "img")):
|
| 74 |
+
if f.endswith(".nii.gz"):
|
| 75 |
+
shutil.move(
|
| 76 |
+
os.path.join("Cervix", "RawData", "Training", "img", f),
|
| 77 |
+
os.path.join("Cervix", "Images", f),
|
| 78 |
+
)
|
| 79 |
+
for f in os.listdir(os.path.join("Cervix", "RawData", "Training", "label")):
|
| 80 |
+
if f.endswith(".nii.gz"):
|
| 81 |
+
shutil.move(
|
| 82 |
+
os.path.join("Cervix", "RawData", "Training", "label", f),
|
| 83 |
+
os.path.join("Cervix", "Masks", f),
|
| 84 |
+
)
|
| 85 |
+
|
| 86 |
+
# Rename final directories
|
| 87 |
+
os.rename("Abdomen", "BCV15-Abdomen")
|
| 88 |
+
os.rename("Cervix", "BCV15-Cervix")
|
| 89 |
+
|
| 90 |
+
# Clean up raw data folders and zip files
|
| 91 |
+
shutil.rmtree(os.path.join("BCV15-Abdomen", "RawData"), ignore_errors=True)
|
| 92 |
+
os.remove(os.path.join("BCV15-Abdomen", "RawData.zip"))
|
| 93 |
+
shutil.rmtree(os.path.join("BCV15-Cervix", "RawData"), ignore_errors=True)
|
| 94 |
+
os.remove(os.path.join("BCV15-Cervix", "CervixRawData.zip"))
|
| 95 |
+
|
| 96 |
+
# Move folder to dataset_dir
|
| 97 |
+
folders_to_move = [
|
| 98 |
+
"BCV15-Cervix",
|
| 99 |
+
"BCV15-Abdomen",
|
| 100 |
+
]
|
| 101 |
+
for folder in folders_to_move:
|
| 102 |
+
move_folder(
|
| 103 |
+
os.path.join(tmp_dir, folder),
|
| 104 |
+
os.path.join(dataset_dir, folder),
|
| 105 |
+
create_dest=True,
|
| 106 |
+
)
|
| 107 |
+
# ====================================
|
| 108 |
+
|
| 109 |
+
print(f"Download and extraction completed for {dataset_name}")
|
| 110 |
+
os.chdir(dataset_dir)
|
| 111 |
+
shutil.rmtree(tmp_dir)
|
| 112 |
+
os.chdir(current_dir)
|
| 113 |
+
|
| 114 |
+
|
| 115 |
+
def main(dir_datasets_data, dataset_name, **kwargs):
|
| 116 |
+
# Create dataset directory
|
| 117 |
+
dataset_dir = os.path.join(dir_datasets_data, dataset_name)
|
| 118 |
+
os.makedirs(dataset_dir, exist_ok=True)
|
| 119 |
+
|
| 120 |
+
# Change to dataset directory
|
| 121 |
+
os.chdir(dataset_dir)
|
| 122 |
+
|
| 123 |
+
# Download and extract dataset
|
| 124 |
+
download_and_extract(dataset_dir, dataset_name, **kwargs)
|
| 125 |
+
|
| 126 |
+
|
| 127 |
+
if __name__ == "__main__":
|
| 128 |
+
# Set up argument parser
|
| 129 |
+
parser = argparse.ArgumentParser(description="Download and extract dataset")
|
| 130 |
+
parser.add_argument(
|
| 131 |
+
"-d",
|
| 132 |
+
"--dir_datasets_data",
|
| 133 |
+
help="Directory path where datasets will be stored",
|
| 134 |
+
required=True,
|
| 135 |
+
)
|
| 136 |
+
parser.add_argument(
|
| 137 |
+
"-n",
|
| 138 |
+
"--dataset_name",
|
| 139 |
+
help="Name of the dataset",
|
| 140 |
+
required=True,
|
| 141 |
+
)
|
| 142 |
+
args = parser.parse_args()
|
| 143 |
+
|
| 144 |
+
main(
|
| 145 |
+
dir_datasets_data=args.dir_datasets_data,
|
| 146 |
+
dataset_name=args.dataset_name,
|
| 147 |
+
)
|