RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging
Paper
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2203.14928
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YAML Metadata
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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
@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
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Size of downloaded dataset files:
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Number of rows:
42