# CT-ORG: Multiple Organ Segmentation in CT

## License
**CC BY 3.0**  
[Creative Commons Attribution 3.0 License](https://creativecommons.org/licenses/by/3.0/)

## Citation
Paper BibTeX:
```bibtex
@article{rister2020ct,
  title={CT-ORG, a new dataset for multiple organ segmentation in computed tomography},
  author={Rister, Blaine and Yi, Darvin and Shivakumar, Kaushik and Nobashi, Tomomi and Rubin, Daniel L},
  journal={Scientific Data},
  volume={7},
  number={1},
  pages={381},
  year={2020},
  publisher={Nature Publishing Group UK London}
}
```

Dataset:

```bibtex
Rister, B., Shivakumar, K., Nobashi, T., & Rubin, D. L. (2019). CT-ORG: A Dataset of CT Volumes With Multiple Organ Segmentations (Version 1) [dataset]. The Cancer Imaging Archive.  DOI: 10.7937/tcia.2019.tt7f4v7o
```


## Dataset description
CT-ORG contains 140 CT scans from diverse sources, each with 3D segmentations of five organs, and brain labels in some cases. The dataset covers a wide range of imaging conditions and includes both benign and malignant liver lesions, as well as metastatic disease in bones and lungs, providing a challenging benchmark for multi-class organ segmentation.

**Number of CT volumes**: 140

**Contrast**: Both contrast-enhanced and non-contrast; includes PET-CT derived scans

**CT body coverage**: Abdominal and full-body

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

**Original GT annotation targets**: Liver, urinary bladder, lungs, kidneys, bone

**Number of annotated CT volumes**: 140

**Annotator**: Human (lungs and bones partly from morphological algorithms)

**Acquisition centers**: Multiple global institutions, Ludwig Maxmilian University of Munich, Radboud University Medical Center of Nijmegen, Poly-technique & CHUM Research Center Montreal, Tel Aviv University, Sheba Medical Center, IRCAD Institute Strasbourg and Hebrew University of Jerusalem. The PET-CT images all derive from Stanford Healthcare.

**Pathology/Disease**: Benign and malignant liver lesions, metastatic disease in bones and lungs

**Original dataset download link**: https://www.cancerimagingarchive.net/collection/ct-org/

**Original dataset format**: nifti