# Segmentation of Retinal Blood Vessels Using Deep Learning

arXiv: https://arxiv.org/abs/2303.09679 · HTML: https://mlsanity.com/paper/2303.09679/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 42 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet](https://mlsanity.com/paper/2111.04739/) (2021)
- [Retinal Image Segmentation with Small Datasets](https://mlsanity.com/paper/2303.05110/) (2023)

## Abstract (arXiv)

The morphology of retinal blood vessels can indicate various diseases in the human body, and researchers have been working on automatic scanning and segmentation of retinal images to aid diagnosis. This project compares the performance of four neural network architectures in segmenting retinal images, using a combined dataset from different databases, namely the UNet, DR-VNet, UNet-ResNet and UNet-VGG.