Awesome

AbdonmenAtlas 1.0

We are proud to introduce AbdomenAtlas-8K, a substantial multi-organ dataset with the spleen, liver, kidneys, stomach, gallbladder, pancreas, aorta, and IVC annotated in 8,448 CT volumes, totaling 3.2 million CT slices.

An endeavor of such magnitude would demand a staggering 1,600 weeks or roughly 30.8 years of an experienced annotator's time.

In contrast, our annotation method has accomplished this task in three weeks (premised on an 8-hour workday, five days a week) while maintaining a similar or even better annotation quality.

Data - AbdomenAtlas1.0Mini

Paper

<b>AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking</b> <br/> Wenxuan Li, Chongyu Qu, Xiaoxi Chen, Pedro R. A. S. Bassi, Yijia Shi, Yuxiang Lai, Qian Yu, Huimin Xue, Yixiong Chen, Xiaorui Lin, Yutong Tang, Yining Cao, Haoqi Han, Zheyuan Zhang, Jiawei Liu, Tiezheng Zhang, Yujiu Ma, Jincheng Wang, Guang Zhang, Alan Yuille, Zongwei Zhou* <br/> Johns Hopkins University <br/> Medical Image Analysis, 2024 <br/> <a href='https://www.zongweiz.com/dataset'><img src='https://img.shields.io/badge/Project-Page-Green'></a> <a href='https://www.cs.jhu.edu/~alanlab/Pubs24/li2024abdomenatlas.pdf'><img src='https://img.shields.io/badge/Paper-PDF-purple'></a>

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

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

★ An improved version, AbdomenAtlas 1.1, can be found at SuPreM .

★ Touchstone - Let's benchmark!

★ We have maintained a document for Frequently Asked Questions.

0. Installation

git clone https://github.com/MrGiovanni/AbdomenAtlas

See installation instructions to create an environment and obtain requirements.

1. Download AI models

We offer pre-trained checkpoints of Swin UNETR and U-Net. The models were trained on a combination of 14 publicly available CT datasets, consisting of 3,410 (see details in CLIP-Driven Universal Model). Download the trained models and save them into ./pretrained_checkpoints/.

2. Prepare your datasets

It can be publicly available datasets (e.g., BTCV) or your private datasets. Currently, we only take data formatted in nii.gz. This repository will help you assign annotations to these datasets, including 25 organs and six types of tumors (where the annotation of eight organs is pretty accurate).

2.1 Download

Taking the BTCV dataset as an example, download this dataset and save it to the datapath directory.

cd $datapath
wget https://www.dropbox.com/s/jnv74utwh99ikus/01_Multi-Atlas_Labeling.tar.gz
tar -xzvf 01_Multi-Atlas_Labeling.tar.gz

2.2 Preprocessing

Generate a list for this dataset.

cd AbdomenAtlas/
python -W ignore generate_datalist.py --data_path $datapath --dataset_name $dataname --folder img --out ./dataset/dataset_list --save_file $dataname.txt

3. Generate masks

U-Net

CUDA_VISIBLE_DEVICES=0 python -W ignore test.py --resume pretrained_checkpoints/unet.pth --backbone unet --save_dir $savepath --dataset_list $dataname --data_root_path $datapath --store_result >> logs/$dataname.unet.txt

Swin UNETR

CUDA_VISIBLE_DEVICES=0 python -W ignore test.py --resume pretrained_checkpoints/swinunetr.pth --backbone swinunetr --save_dir $savepath --dataset_list $dataname --data_root_path $datapath --store_result >> logs/$dataname.swinunetr.txt

To generate attention maps for the active learning process (Step 5 [optional]), remember to save entropy and soft predictions by using the options --store_entropy and --store_soft_pred

4. Data Assembly

In the assembly process, our utmost priority is given to the original annotations supplied by each public dataset. Subsequently, we assign secondary priority to the revised labels from our annotators. The pseudo labels, generated by AI models, are accorded the lowest priority. The following code can implement this priority into the assembled dataset.

python -W ignore assemble.py --data_path $savepath --dataset_name $dataname --backbone swinunetr --save_dir SAVE_DIR --version V1

This is how our AbdonmenAtlas-8K appears

    $savepath/
    ├── $dataname_img0001
    ├── $dataname_img0002
    ├── $dataname_img0003
        │── ct.nii.gz
        ├── original_label.nii.gz
        ├── pseudo_label.nii.gz
        └── segmentations
            ├── spleen.nii.gz
            ├── liver.nii.gz
            ├── pancreas.nii.gz

5. [Optional] Active Learning

If you want to perform the active learning process, you will need the following active learning instructions to generate the attention map for human annotators.

TODO

• Release pre-trained AI model checkpoints (U-Net and Swin UNETR)
• Release the AbdomenAtlas-8K dataset (we commit to releasing 3,410 of the 8,448 CT volumes)
• Support more data formats (e.g., dicom)

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 Ye, Jin and He, Junjun and Wald, Tassilo and Ulrich, Constantin and Baumgartner, Michael and Roy, Saikat and Maier-Hein, Klaus H. and Jaeger, Paul and Ye, Yiwen and Xie, Yutong and Zhang, Jianpeng and Chen, Ziyang and Xia, Yong and Xing, Zhaohu and Zhu, Lei and Sadegheih, Yousef and Bozorgpour, Afshin and Kumari, Pratibha and Azad, Reza and Merhof, Dorit and Shi, Pengcheng and Ma, Ting and Du, Yuxin and Bai, Fan and Huang, Tiejun and Zhao, Bo and Wang, Haonan and Li, Xiaomeng and Gu, Hanxue and Dong, Haoyu and Yang, Jichen and Mazurowski, Maciej A. and Gupta, Saumya and Wu, Linshan and Zhuang, Jiaxin and Chen, Hao and Roth, Holger and Xu, Daguang and Blaschko, Matthew B. and Decherchi, Sergio and Cavalli, Andrea and Yuille, Alan L. and Zhou, Zongwei},
  journal={Conference on Neural Information Processing Systems},
  year={2024},
  utl={https://github.com/MrGiovanni/Touchstone}
}

@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 and others},
  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.