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0007_chaos/README_0007_chaos.md
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# CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge (CT Subset)
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## License
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**CC BY-NC-SA 4.0**
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[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/)
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## Citation
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Paper BibTeX:
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```bibtex
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@article{kavur2021chaos,
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title={CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation},
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author={Kavur, A Emre and Gezer, N Sinem and Bar{\i}{\c{s}}, Mustafa and Aslan, Sinem and Conze, Pierre-Henri and Groza, Vladimir and Pham, Duc Duy and Chatterjee, Soumick and Ernst, Philipp and {\"O}zkan, Sava{\c{s}} and others},
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journal={Medical image analysis},
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volume={69},
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pages={101950},
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year={2021},
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publisher={Elsevier}
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}
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```
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Dataset BibTeX:
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```bibtex
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@dataset{CHAOSdata2019,
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author = {Ali Emre Kavur and M. Alper Selver and Oğuz Dicle and Mustafa Barış and N. Sinem Gezer},
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title = {{CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data}},
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month = Apr,
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year = 2019,
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publisher = {Zenodo},
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version = {v1.03},
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doi = {10.5281/zenodo.3362844},
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url = {https://doi.org/10.5281/zenodo.3362844}
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}
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```
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## Dataset description
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The CHAOS challenge, held at ISBI 2019, aimed to benchmark abdominal organ segmentation across CT and MRI modalities in healthy subjects. It provides CT and MR scans with corresponding annotations to support research in single- and multi-modal segmentation, enabling comparative analysis of deep learning methods.
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**Challenge homepage**: https://chaos.grand-challenge.org/
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**Number of CT volumes**: 40
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**Contrast**: Contrast-enhanced (portal venous phase)
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**CT body coverage**: Abdomen
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**Does the dataset include any ground truth annotations?**: Yes
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**Original GT annotation targets**: Liver
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**Number of annotated CT volumes**: 20
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**Annotator**: Human
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**Acquisition centers**: Department of Radiology at Dokuz Eylul University Hospital, Izmir, Turkey
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**Pathology/Disease**: Healthy abdominal organs without pathological abnormalities
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**Original dataset download link**: https://zenodo.org/records/3431873
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**Original dataset format**: DICOM
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