Fully Automated Artery-Vein ratio and vascular tortuosity measurement in retinal fundus images
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Abstract (arXiv)
Accurate measurements of abnormalities like Artery-Vein ratio and tortuosity in fundus images is an actively researched task. Most of the research seems to compute such features independently. However, in this work, we have devised a fully automated technique to measure any vascular abnormalities. This paper is a follow-up paper on vessel topology estimation and extraction, we use the extracted topology to perform A-V state-of-the-art Artery-Vein classification, AV ratio calculation, and vessel tortuosity measurement, all fully automated. Existing techniques tend to only work on the partial region, but we extract the complete vascular structure. We have shown the usability of this topology by extracting two of the most important vascular features; Artery-Vein ratio, and vessel tortuosity.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 35 references that are in the map. First posted on arXiv 4 Jan 2023.
Builds on
6 of 35 references.
References · 6
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2020Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classificationcited by 13
- 2019Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Gradingcited by 12
- 2019Dynamic Deep Networks for Retinal Vessel Segmentationcited by 9
- 2021Optic Disc Segmentation using Disk-Centered Patch Augmentationcited by 2
- 2022Fully Automated Tree Topology Estimation and Artery-Vein Classificationcited by 1
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Citing papers per year: 2024: 1.
1 paper.