# RAVIR Dataset

RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging.

## 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

## Classes
- 0: Background
- 128: Arteries
- 256: Veins (stored as 255 in uint8)

## Splits
- **Train**: 23 images with segmentation masks
- **Test**: 19 images (masks withheld for challenge evaluation)

## 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}
}
```

## License
CC BY-NC-SA 4.0 (Non-commercial use only)

## Links
- [Grand Challenge](https://ravir.grand-challenge.org/)
- [Paper (arXiv)](https://arxiv.org/abs/2203.14928)
- [Paper (IEEE)](https://ieeexplore.ieee.org/document/9744459)