0003_kits21/README_0003_kits21.md

# The Kidney and Kidney Tumor Segmentation Challenge （KiTS21）

## License
**CC BY-NC-SA 4.0**  
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/)


## Citation
Paper BibTeX:
```bibtex
@article{heller2021state,
  title={The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge},
  author={Heller, Nicholas and Isensee, Fabian and Maier-Hein, Klaus H and Hou, Xiaoshuai and Xie, Chunmei and Li, Fengyi and Nan, Yang and Mu, Guangrui and Lin, Zhiyong and Han, Miofei and others},
  journal={Medical image analysis},
  volume={67},
  pages={101821},
  year={2021},
  publisher={Elsevier}
}
```

## Dataset description
KiTS21 builds on the KiTS19 challenge, which aimed to advance automatic 3D kidney and kidney tumor segmentation in contrast-enhanced CT scans. It provides a curated set of manually annotated volumes for benchmarking deep learning methods and supports an open leaderboard for ongoing evaluation.

**KiTS21 challenge homepage**: https://kits-challenge.org/kits23/

**KiTS21 challenge design**: https://zenodo.org/records/4674397

**Number of CT volumes**: 300

**Contrast**: Contrast-enhanced

**CT body coverage**: Abdomen (occasional chest/pelvis coverage)

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

**Original GT annotation targets**: Kidney, kidney tumor, kidney cyst

**Number of annotated CT volumes**: 300

**Annotator**: Human

**Acquisition centers**: Multiple, with varied scanner brands; predominantly from Minnesota, North Dakota, and western Wisconsin

**Pathology/Disease**: Kidney tumors

**Original dataset download link**: https://github.com/neheller/kits21/blob/master/README.md

**Original dataset format**: nifti

## Note
These 300 volumes correspond to the KiTS21 training split, which includes all cases from the train and test splits of KiTS19.