AbdomenAtlas/AbdomenAtlas1.0Mini

Dataset Summary

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).

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Join the AbdomenAtlas Benchmarking Project

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 annotated CT dataset to date. Subsequently, we will evaluate the trained models using a large internal dataset at Johns Hopkins University. Contributors to this large-scale project will be offered authorship in the resulting paper. 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.

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Downloading Instructions

1- Install the Hugging Face library:

pip install -U "huggingface_hub[cli]"

2- Download the dataset:

mkdir AbdomenAtlas
cd AbdomenAtlas
huggingface-cli download AbdomenAtlas/AbdomenAtlas1.0Mini  --repo-type dataset --local-dir .  --cache-dir ./cache

[Optional] Resume downloading

In case you had a previous interrupted download, resume it by adding “--resume-download” to the download command:

huggingface-cli download AbdomenAtlas/AbdomenAtlas1.0Mini  --repo-type dataset --local-dir .  --cache-dir ./cache --resume-download

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,*
1 Johns Hopkins University,
² Rutgers University,
³ City University of Hong Kong,
⁴ NVIDIA
NeurIPS 2023
paper | code | dataset | annotation | poster

AbdomenAtlas-8K: Human-in-the-Loop Annotating Eight Anatomical Structures for 8,448 Three-Dimensional Computed Tomography Volumes in Three Weeks
Chongyu Qu¹, Tiezheng Zhang¹, Hualin Qiao², Jie Liu³, Yucheng Tang⁴, Alan L. Yuille¹, and Zongwei Zhou^1,*
1 Johns Hopkins University,
² Rutgers University,
³ City University of Hong Kong,
⁴ NVIDIA
RSNA 2023 (Oral Presentation)
paper | code | slides

Citation

@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.

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.