0009_abdomenct1k/README_0009_abdomenct1k.md

# AbdomenCT-1K

## License
**CC BY 4.0**  
[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)

## Citation
Paper BibTeX:

```bibtex
@article{ma2021abdomenct,
  title={Abdomenct-1k: Is abdominal organ segmentation a solved problem?},
  author={Ma, Jun and Zhang, Yao and Gu, Song and Zhu, Cheng and Ge, Cheng and Zhang, Yichi and An, Xingle and Wang, Congcong and Wang, Qiyuan and Liu, Xin and others},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={44},
  number={10},
  pages={6695--6714},
  year={2021},
  publisher={IEEE}
}
```


## Dataset description
AbdomenCT-1K is a large, diverse abdominal CT organ segmentation dataset with over 1,000 scans from 12 medical centers, covering multiple phases, vendors, and diseases. It serves as a benchmark to reveal and address the limited generalization of state-of-the-art methods, providing tasks for fully, semi-, weakly supervised, and continual learning research.

**Challenge homepage**: https://abdomenct-1k-fully-supervised-learning.grand-challenge.org/

**Number of CT volumes**: 1062

**Contrast**: Contrast-enhanced (multi-phase: plain, arterial, portal)

**CT body coverage**: Abdomen

**Does the dataset include any ground truth annotations?**: Yes

**Original GT annotation targets**: Liver, spleen, kidney, pancreas

**Number of annotated CT volumes**: 1000

**Annotator**: Initial model + manual refinement

**Acquisition centers**: 12 medical centers

**Pathology/Disease**: Lesions in one or more labeled organs, including benign/malignant liver lesions and cancers of the pancreas, colon, and liver

**Original dataset download link**:

Part 1: https://zenodo.org/records/5903099

Part 2: https://zenodo.org/records/5903846

Part 3: https://zenodo.org/records/5903769

**Original dataset format**: nifti