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Terms and Conditions for Using the AbdomenAtlas Dataset
1. Acceptance of Terms
Accessing and using the AbdomenAtlas dataset implies your agreement to these terms and conditions. If you disagree with any part, please refrain from using the dataset.
2. Permitted Use
- The dataset is intended solely for academic, research, and educational purposes.
- Any commercial exploitation of the dataset without prior permission is strictly forbidden.
- You must adhere to all relevant laws, regulations, and research ethics, including data privacy and protection standards.
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- Acknowledge the presence of sensitive information within the dataset and commit to maintaining data confidentiality.
- Direct attempts to re-identify individuals from the dataset are prohibited.
- Ensure compliance with data protection laws such as GDPR and HIPAA.
4. Attribution
- Cite the dataset and acknowledge the providers in any publications resulting from its use.
- Claims of ownership or exclusive rights over the dataset or derivatives are not permitted.
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- Redistribution of the dataset or any portion thereof is not allowed.
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The dataset is provided "as is" without warranty of any kind, either expressed or implied, including but not limited to the accuracy or completeness of the data.
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Dataset Summary
Per-voxel annotations for AbdomenAtlas X.
Downloading Instructions
1- Install the Hugging Face library:
pip install huggingface_hub[hf_transfer]==0.24.0
HF_HUB_ENABLE_HF_TRANSFER=1
[Optional] Alternative without HF Trasnsfer (slower)
pip install huggingface_hub==0.24.0
2- Download the dataset:
mkdir AbdomenAtlas
cd AbdomenAtlas
huggingface-cli download BodyMaps/AbdomenAtlasXMiniMask --repo-type dataset --local-dir .
[Optional] Resume downloading
In case you had a previous interrupted download, just run the huggingface-cli download command above again.
huggingface-cli download BodyMaps/AbdomenAtlasXMiniMask --repo-type dataset --local-dir .
Citation
@article{li2024abdomenatlas,
title={AbdomenAtlas: A large-scale, detailed-annotated, \& multi-center dataset for efficient transfer learning and open algorithmic benchmarking},
author={Li, Wenxuan and Qu, Chongyu and Chen, Xiaoxi and Bassi, Pedro RAS and Shi, Yijia and Lai, Yuxiang and Yu, Qian and Xue, Huimin and Chen, Yixiong and Lin, Xiaorui and others},
journal={Medical Image Analysis},
pages={103285},
year={2024},
publisher={Elsevier},
url={https://github.com/MrGiovanni/AbdomenAtlas}
}
@article{bassi2024touchstone,
title={Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?},
author={Bassi, Pedro RAS and Li, Wenxuan and Tang, Yucheng and Isensee, Fabian and Wang, Zifu and Chen, Jieneng and Chou, Yu-Cheng and Kirchhoff, Yannick and Rokuss, Maximilian and Huang, Ziyan and others},
journal={arXiv preprint arXiv:2411.03670},
year={2024},
url={https://github.com/MrGiovanni/RadGPT}
}
@inproceedings{li2024well,
title={How Well Do Supervised Models Transfer to 3D Image Segmentation?},
author={Li, Wenxuan and Yuille, Alan and Zhou, Zongwei},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://github.com/MrGiovanni/SuPReM}
}
@article{qu2023abdomenatlas,
title={Abdomenatlas-8k: Annotating 8,000 CT volumes for multi-organ segmentation in three weeks},
author={Qu, Chongyu and Zhang, Tiezheng and Qiao, Hualin and Tang, Yucheng and Yuille, Alan L and Zhou, Zongwei},
journal={Advances in Neural Information Processing Systems},
volume={36},
year={2023},
url={https://github.com/MrGiovanni/AbdomenAtlas}
}
Acknowledgements
This work was supported by the Lustgarten Foundation for Pancreatic Cancer Research and partially by the Patrick J. McGovern Foundation Award. We appreciate the effort of the MONAI Team to provide open-source code for the community.
License
AbdomenAtlas 1.1 is licensed under CC BY-NC-SA 4.0.
Uploading AbdomenAtlas to HuggingFace
The file AbdomenAtlasUploadMultipleFolders.ipynb has the code we used to upload AbdomenAtlas to Hugging Face. It may be ncessary to run the script multiple times, until it finishes without an uploading error. The uploading script requires PyTorch, huggingface_hub, and Jupyter Notebook.
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