Domain Agnostic Pipeline for Retina Vessel Segmentation
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Abstract (arXiv)
Automatic segmentation of retina vessels plays a pivotal role in clinical diagnosis of prevalent eye diseases, such as, Diabetic Retinopathy or Age-related Macular Degeneration. Due to the complex construction of blood vessels, with drastically varying thicknesses, accurate vessel segmentation can be quite a challenging task. In this work we show that it is possible to achieve near state-of-the-art performance, by crafting a careful thought pre-processing pipeline, without having to resort to complex networks and/or training routines. We also show that our model is able to maintain the same high segmentation performance across different datasets, very poor quality fundus images, as well as images of severe pathological cases. Code and models featured in this paper can be downloaded from http://github.com/farrell236/retina_segmentation. We also demonstrate the potential of our model at http://lazarus.ddns.net:8502.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 15 references that are in the map. First posted on arXiv 18 Feb 2023.
Builds on
5 of 15 references.
References · 5
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
- 2018Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentationcited by 180
- 2018DUNet: A deformable network for retinal vessel segmentationcited by 89
- 2021RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Networkcited by 24
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Citing papers per year: 2023: 1.
1 paper.