You need to agree to share your contact information to access this dataset
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
Terms and Conditions for Using the AbdomenAtlas 1.0 Mini Dataset
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.
Log in or Sign Up to review the conditions and access this dataset content.
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 noisy labels for aorta and kidneys
Downloading Instructions
1- Register at Huggingface, accept our terms and conditions, and create an access token:
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 -U "huggingface_hub[cli]"
3- Download the dataset:
mkdir AbdomenAtlas
cd AbdomenAtlas
huggingface-cli download AbdomenAtlas/AbdomenAtlas1.0Mini --token paste_your_token_here --repo-type dataset --local-dir . --cache-dir ./cache --local-dir-use-symlinks False
[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 --token paste_your_token_here --repo-type dataset --local-dir . --cache-dir ./cache --resume-download --local-dir-use-symlinks False
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
@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.
- Downloads last month
- 2