1. Acceptance of Terms
Accessing and using the AbdomenAtlas 1.0 Mini 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.

3. Data Protection and Privacy

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.

5. Redistribution

Redistribution of the dataset or any portion thereof is not allowed.
Sharing derived data must respect the privacy and confidentiality terms set forth.

6. Disclaimer
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.

7. Limitation of Liability
Under no circumstances will the dataset providers be liable for any claims or damages resulting from your use of the dataset.

8. Access Revocation
Violation of these terms may result in the termination of your access to the dataset.

9. Amendments
The terms and conditions may be updated at any time; continued use of the dataset signifies acceptance of the new terms.

10. Governing Law
These terms are governed by the laws of the location of the dataset providers, excluding conflict of law rules.

Consent:
Accessing and using the AbdomenAtlas 1.0 Mini dataset signifies your acknowledgment and agreement to these terms and conditions.

Dataset Summary

One of the largest, fully-annotated CT dataset to date, including 5,195 annotated CT volumes (with spleen, liver, kidneys, stomach, gallbladder, pancreas, aorta, and IVC annotations).

Join the AbdomenAtlas Benchmarking Project (Touchstone)

The Benchmarking Project aims to compare diverse semantic segmentation and pre-training algorithms. We, the CCVL research group at Johns Hopkins University, invite creators of these algorithms to contribute to the initiative. With our support, contributors will train their methodologies on the largest fully-annotated abdominal CT datasets to date. Subsequently, we will evaluate the trained models using a large internal dataset at Johns Hopkins University. If you are the creator of a semantic segmentation or pre-training algorithm and wish to advance medical AI by participating in the Benchmark Project, please reach out to pedro.salvadorbassi2@unibo.it. We will provide you further details on the project and explain your opportunities to collaborate in our future publications!

Note for Touchstone Benchmarking Project

Touchstone Benchmarking Project participants should not use this dataset. The version used in the project is now in AbdomenAtlas1.0MiniBeta. This version (AbdomenAtlas/AbdomenAtlas1.0Mini) was updated with improved aorta and kidney annotations. Thus, AI algorithms trained in this dataset can be directly compared to the results in the Touchstone Project, except for these 2 organs.

Dataset variants:

AbdomenAtlas1.0Mini - 5,195 annotated CT volumes, improved label quality for aorta and kidneys

_AbdomenAtlas1.0Mini - same as above, but structured as large zip files, to facilitate downloading

AbdomenAtlas1.0MiniBeta - 5,195 annotated CT volumes, with the noisy labels for aorta and kidneys

Downloading Instructions

1- Register at Huggingface, accept our terms and conditions, and create an access token:

Create a Huggingface account

Accept our terms and conditions for acessing this dataset (top of this page)

Create a Huggingface access token and copy it (you will use it in step 3, in paste_your_token_here)

2- Install the Hugging Face library:

pip install huggingface_hub[hf_transfer]==0.24.0
HF_HUB_ENABLE_HF_TRANSFER=1
pip install ipywidgets

[Optional] Alternative without HF Trasnsfer (slower)

pip install huggingface_hub==0.24.0

3- Download the dataset:

mkdir AbdomenAtlas
cd AbdomenAtlas
huggingface-cli download AbdomenAtlas/_AbdomenAtlas1.0Mini --token paste_your_token_here --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 AbdomenAtlas/_AbdomenAtlas1.0Mini --token paste_your_token_here --repo-type dataset --local-dir .

4- Uncompress:

Uncompress:

bash unzip.sh

Check if the folder AbdomenAtlas/uncompressed contains all cases, from BDMAP_00000001 to BDMAP_00005195. If so, you can delete the original compressed files, running:

bash delete.sh

Paper

AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks
Chongyu Qu¹, Tiezheng Zhang¹, Hualin Qiao², Jie Liu³, Yucheng Tang⁴, Alan L. Yuille¹, and Zongwei Zhou^1,*
¹Johns Hopkins University,
²Rutgers University,
³City University of Hong Kong,
⁴NVIDIA
NeurIPS 2023
paper | code | dataset | annotation | poster

How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?
Wenxuan Li, Alan Yuille, and Zongwei Zhou^*
Johns Hopkins University
International Conference on Learning Representations (ICLR) 2024 (oral; top 1.2%)
paper | code

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{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}
}

@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}
}

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 1.0 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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