Metadata-Version: 2.1
Name: AbdomenAtlas
Version: 0.1.1
Summary: A package for 3D medical image segmentation using deep learning techniques.
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: connected-components-3d==3.18.0
Requires-Dist: fastremap==1.15.0
Requires-Dist: h5py==3.11.0
Requires-Dist: imageio==2.35.1
Requires-Dist: matplotlib==3.9.2
Requires-Dist: monai==1.2.0
Requires-Dist: nibabel==5.2.1
Requires-Dist: numpy==2.1.0
Requires-Dist: opencv-python==4.10.0.84
Requires-Dist: pandas==2.2.2
Requires-Dist: Pillow==10.4.0
Requires-Dist: scipy==1.14.1
Requires-Dist: tensorboardX==2.6.2.2
Requires-Dist: torch==2.4.0
Requires-Dist: tqdm==4.66.5

# 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

     Option
     Image & Mask
     Image-Only
     Mask-Only

     Huggingface
     All-in-one

     Download (3.3GB)

A single compressed file containing all labels.

     Zips
     Download (328GB) (https://huggingface.co/datasets/AbdomenAtlas/_AbdomenAtlas1.0Mini)

11 compressed files in TAR.GZ format.

     Download

11 compressed files in TAR.GZ format.

     Folders
     Download (340GB) (https://huggingface.co/datasets/AbdomenAtlas/AbdomenAtlas1.0Mini)

5,195 folders, each containing CT, combined labels, and a segmentations folder of nine masks in NII.GZ format.

     Download (325GB)

5,195 folders, each containing CT in NII.GZ format.

     Download (15GB)

5,195 folders, each containing combined labels, and a segmentations folder of nine masks in NII.GZ format.

     Dropbox
     All-in-one
     Download (306GB) (https://www.dropbox.com/scl/fi/bqk780hn794gep6upfrge/AbdomenAtlas1.0Mini.tar.gz?rlkey=gjiiz731us18p9u7iouhin3gw&dl=0)

A single compressed file containing all images and labels.

     Download (303GB) (https://www.dropbox.com/scl/fi/v6esel9mun1k32mpmo690/AbdomenAtlas1.0Mini.tar.gz?rlkey=b1mxdmvasr9znpp31cup9eqhk&dl=0)

A single compressed file containing all images.

     Download (3.3GB) (https://www.dropbox.com/scl/fi/v6esel9mun1k32mpmo690/AbdomenAtlas1.0Mini.tar.gz?rlkey=b1mxdmvasr9znpp31cup9eqhk&dl=0)

A single compressed file containing all labels.

     Zips
     Download (328GB) (https://www.dropbox.com/scl/fo/jy4sf9mk3zzlty9qfhgx6/AAAJKO1bYvjL5J3CrU-w2rs?rlkey=j54u9eu968rw3ntd7bc4e5wd4&dl=0)

11 compressed files in TAR.GZ format.

     Download

11 compressed files in TAR.GZ format.

     Baidu Wangpan
     All-in-one
     Download (306GB) (https://pan.baidu.com/s/1MtVfCVu3bk0Vd5GcR1qXhQ?pwd=0317)

A single compressed file containing all images and labels.

     Download (303GB) (https://pan.baidu.com/s/1-HQO-bYmeC0xFusqe-DwcA?pwd=0317)

A single compressed file containing all images.

     Download (3.3GB) (https://pan.baidu.com/s/1r3f4AhSffgIGauI0wRGEtw?pwd=0317)

A single compressed file containing all labels.

     Zips
     Download (328GB) (https://pan.baidu.com/s/1AdJmhFXPDua63wQ_zAEEiQ?pwd=0317)

11 compressed files in TAR.GZ format.

     Download

11 compressed files in TAR.GZ format.

## Paper

AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking 
[Wenxuan Li](https://scholar.google.com/citations?hl=en&user=tpNZM2YAAAAJ), [Chongyu Qu](https://github.com/Chongyu1117), 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](https://www.cs.jhu.edu/~ayuille/), [Zongwei Zhou](https://www.zongweiz.com/)* 
Johns Hopkins University 
Medical Image Analysis, 2024 

AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks 
[Chongyu Qu](https://github.com/Chongyu1117)1, [Tiezheng Zhang](https://github.com/ollie-ztz)1, [Hualin Qiao](https://www.linkedin.com/in/hualin-qiao-a29438210/)2, [Jie Liu](https://ljwztc.github.io/)3, [Yucheng Tang](https://scholar.google.com/citations?hl=en&user=0xheliUAAAAJ)4, [Alan L. Yuille](https://www.cs.jhu.edu/~ayuille/)1, and [Zongwei Zhou](https://www.zongweiz.com/)1,* 
1 Johns Hopkins University,  
2 Rutgers University,  
3 City University of Hong Kong,   
4 NVIDIA 
NeurIPS 2023 
[paper](https://www.cs.jhu.edu/~alanlab/Pubs23/qu2023abdomenatlas.pdf) | [code](https://github.com/MrGiovanni/AbdomenAtlas) | [dataset](https://huggingface.co/datasets/AbdomenAtlas/AbdomenAtlas1.0Mini) | [poster](document/neurips_poster.pdf)

AbdomenAtlas-8K: Human-in-the-Loop Annotating Eight Anatomical Structures for 8,448 Three-Dimensional Computed Tomography Volumes in Three Weeks 
[Chongyu Qu](https://github.com/Chongyu1117)1, [Tiezheng Zhang](https://github.com/ollie-ztz)1, [Hualin Qiao](https://www.linkedin.com/in/hualin-qiao-a29438210/)2, [Jie Liu](https://ljwztc.github.io/)3, [Yucheng Tang](https://scholar.google.com/citations?hl=en&user=0xheliUAAAAJ)4, [Alan L. Yuille](https://www.cs.jhu.edu/~ayuille/)1, and [Zongwei Zhou](https://www.zongweiz.com/)1,* 
1 Johns Hopkins University,  
2 Rutgers University,  
3 City University of Hong Kong,   
4 NVIDIA 
RSNA 2023 (Oral Presentation) 
[paper](document/rsna_abstract.pdf) | [code](https://github.com/MrGiovanni/AbdomenAtlas) | [slides](document/rsna_slides.pdf)

**★ An improved version, AbdomenAtlas 1.1, can be found at [SuPreM](https://github.com/MrGiovanni/SuPreM) [![GitHub stars](https://img.shields.io/github/stars/MrGiovanni/SuPreM.svg?logo=github&label=Stars)](https://github.com/MrGiovanni/SuPreM).**

**★ Touchstone - [Let's benchmark!](https://www.cs.jhu.edu/~zongwei/advert/TutorialBenchmarkV1.pdf) [![GitHub stars](https://img.shields.io/github/stars/MrGiovanni/Touchstone.svg?logo=github&label=Stars)](https://github.com/MrGiovanni/Touchstone)**

**★ We have maintained a document for [Frequently Asked Questions](document/frequently_asked_questions.md).**

## 0. Installation

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

See [installation instructions](document/INSTALL.md) 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](https://github.com/ljwztc/CLIP-Driven-Universal-Model)).
Download the trained models and save them into `./pretrained_checkpoints/`.

| Architecture | Param | Download |
|  ----  | ----  |  ----  |
| U-Net  | 19.08M | [link](https://www.dropbox.com/s/lyunaue0wwhmv5w/unet.pth) |
| Swin UNETR | 62.19M | [link](https://www.dropbox.com/s/jdsodw2vemsy8sz/swinunetr.pth) |

## 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.
```bash
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.

```bash
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
```bash
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
```bash
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.

```bash
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](document/active_learning_instructions.md) to generate the attention map for human annotators.

Figure. Illustration of an attention map.

## TODO

- [x] Release pre-trained AI model checkpoints (U-Net and Swin UNETR)
- [x] 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{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.
