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| # RAVIR Dataset |
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| RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging. |
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| ## Dataset Information |
| - **Modality**: Infrared (815nm) Scanning Laser Ophthalmoscopy (SLO) |
| - **Image Size**: 768×768 pixels |
| - **Format**: PNG |
| - **Camera**: Heidelberg Spectralis with 30° FOV |
| - **Pixel Resolution**: 12.5 microns per pixel |
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| ## Classes |
| - 0: Background |
| - 128: Arteries |
| - 256: Veins (stored as 255 in uint8) |
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| ## Splits |
| - **Train**: 23 images with segmentation masks |
| - **Test**: 19 images (masks withheld for challenge evaluation) |
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| ## Citation |
| ```bibtex |
| @article{hatamizadeh2022ravir, |
| title={RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging}, |
| author={Hatamizadeh, Ali and Hosseini, Hamid and Patel, Niraj and Choi, Jinseo and Pole, Cameron and Hoeferlin, Cory and Schwartz, Steven and Terzopoulos, Demetri}, |
| journal={IEEE Journal of Biomedical and Health Informatics}, |
| year={2022}, |
| publisher={IEEE} |
| } |
| ``` |
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| ## License |
| CC BY-NC-SA 4.0 (Non-commercial use only) |
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| ## Links |
| - [Grand Challenge](https://ravir.grand-challenge.org/) |
| - [Paper (arXiv)](https://arxiv.org/abs/2203.14928) |
| - [Paper (IEEE)](https://ieeexplore.ieee.org/document/9744459) |
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