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# HaN-Seg: The head and neck organ-at-risk CT & MR segmentation dataset |
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## License |
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**CC BY-NC-ND 4.0** |
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[Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License](https://creativecommons.org/licenses/by-nc-nd/4.0/) |
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## Citation |
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Paper BibTeX: |
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```bibtex |
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@article{podobnik2023han, |
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title={HaN-Seg: The head and neck organ-at-risk CT and MR segmentation dataset}, |
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author={Podobnik, Ga{\v{s}}per and Strojan, Primo{\v{z}} and Peterlin, Primo{\v{z}} and Ibragimov, Bulat and Vrtovec, Toma{\v{z}}}, |
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journal={Medical physics}, |
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volume={50}, |
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number={3}, |
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pages={1917--1927}, |
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year={2023}, |
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publisher={Wiley Online Library} |
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} |
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``` |
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## Dataset description |
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HaN-Seg provides anonymized CT and T1-weighted MR scans of 42 head and neck cancer patients acquired for image-guided radiotherapy planning. Each CT scan includes expert-curated binary masks for 30 organs-at-risk (OARs), enabling research on multimodal image analysis and precise radiotherapy planning. |
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**Challenge homepage**: https://han-seg2023.grand-challenge.org/ |
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**Number of CT volumes**: 42 |
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**Contrast**: - |
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**CT body coverage**: Head and neck |
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**Does the dataset include any ground truth annotations?**: Yes |
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**Original GT annotation targets**: (30 OARs) arytenoids, brainstem, carotid artery, cervical esophagus, cochlea, cricopharyngeal inlet, lacrimal gland, larynx—glottis, larynx—supraglottic, lips, mandible, optic chiasm, optic nerve, oral cavity, parotid gland, pituitary gland, spinal cord, submandibular gland, and thyroid |
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**Number of annotated CT volumes**:42 |
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**Annotator**: Human |
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**Acquisition centers**: Institute of Oncology Ljubljana, Slovenia |
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**Pathology/Disease**: - |
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**Original dataset download link**: https://zenodo.org/records/7442914#.ZBtfBHbMJaQ |
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**Original dataset format**: nrrd |
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## Note |
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In accordance with the license, CADS does not redistribute HaN-Seg images or any derivative works. We are currently working on obtaining permission from the dataset authors to share example images with our segmentations. |
