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Datasets:
AbdomenAtlas
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_AbdomenAtlas1.1Mini

Tasks:
Image Segmentation
Modalities:
Text
Formats:
webdataset
Size:
100K - 1M
Tags:
medical
Libraries:
Datasets
WebDataset
License:
Dataset card Viewer Files Community
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Terms and Conditions for Using the AbdomenAtlas 1.1 Mini Dataset

1. Acceptance of Terms
Accessing and using the AbdomenAtlas 1.1 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.

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  • 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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Dataset Summary

The largest, fully-annotated abdominal CT dataset to date, including 9,262 CT volumes with annotations for 25 different anatomical structures.

Join the Touchstone Benchmarking Project

The Touchstone 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 [email protected]. We will provide you further details on the project and explain your opportunities to collaborate in our future publications!

Downloading Instructions

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

Create a Huggingface account

Log in

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
[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/_AbdomenAtlas1.1Mini --token paste_your_token_here --repo-type dataset --local-dir .
Download CTs for BDMAP_00005196 to BDMAP_00009262

For cases BDMAP_00005196 to BDMAP_00009262, the CT scans are from the RSNA Trauma Dataset. Please follow these steps:

1- Download CT Scans

2- Dataset Conversion

  • Convert the downloaded CTs from DICOM format to NIfTI format.
  • Rename each case using the provided RSNATraumaIDMappings.csv file.
  • Format these CTs according to the AbdomenAtlas1.1 structure as shown below.

3- Standardization

  • Standardize the CTs using the Python script available here.

4- Integrate Segmentations

  • Integrate the corresponding segmentations folder downloaded from this Hugging Face repository into each case folder.
AbdomenAtlas1.1
    ├── BDMAP_00000001
    │   ├── ct.nii.gz
    │   └── segmentations
    │       ├── aorta.nii.gz
    │       ├── gall_bladder.nii.gz
    │       ├── kidney_left.nii.gz
    │       ├── kidney_right.nii.gz
    │       ├── liver.nii.gz
    │       ├── pancreas.nii.gz
    │       ├── postcava.nii.gz
    │       ├── spleen.nii.gz
    │       ├── stomach.nii.gz
    │       └── ...
    ├── BDMAP_00000002
    │   ├── ct.nii.gz
    │   └── segmentations
    │       ├── aorta.nii.gz
    │       ├── gall_bladder.nii.gz
    │       ├── kidney_left.nii.gz
    │       ├── kidney_right.nii.gz
    │       ├── liver.nii.gz
    │       ├── pancreas.nii.gz
    │       ├── postcava.nii.gz
    │       ├── spleen.nii.gz
    │       ├── stomach.nii.gz
    │       └── ...
    ├── BDMAP_00000003
    │   ├── ct.nii.gz
    │   └── segmentations
    │       ├── aorta.nii.gz
    │       ├── gall_bladder.nii.gz
    │       ├── kidney_left.nii.gz
    │       ├── kidney_right.nii.gz
    │       ├── liver.nii.gz
    │       ├── pancreas.nii.gz
    │       ├── postcava.nii.gz
    │       ├── spleen.nii.gz
    │       ├── stomach.nii.gz
    │       └── ...
    ...
[Optional] Resume downloading

In case you had a previous interrupted download, just run the huggingface-cli download command above again.

huggingface-cli download AbdomenAtlas/_AbdomenAtlas1.1Mini --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_00009262. If so, you can delete the original compressed files, running:

bash delete.sh

Class map

Meaning of the integer voxel values in the "combined_labels.nii.gz" files:

class_map = {1: 'aorta', 2: 'gall_bladder', 3: 'kidney_left', 4: 'kidney_right', 5: 'liver',
             6: 'pancreas', 7: 'postcava', 8: 'spleen', 9: 'stomach', 10: 'adrenal_gland_left',
             11: 'adrenal_gland_right', 12: 'bladder', 13: 'celiac_trunk', 14: 'colon', 15: 'duodenum',
             16: 'esophagus', 17: 'femur_left', 18: 'femur_right', 19: 'hepatic_vessel', 20: 'intestine',
             21: 'lung_left', 22: 'lung_right', 23: 'portal_vein_and_splenic_vein',
             24: 'prostate', 25: 'rectum'}

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 Zhou1,*
1 Johns Hopkins University,
2 Rutgers University,
3 City University of Hong Kong,
4 NVIDIA
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 

@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}
}

@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}
}

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.

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