# LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images

arXiv: https://arxiv.org/abs/2309.05780 · HTML: https://mlsanity.com/paper/2309.05780/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Efficient Object Localization Using Convolutional Networks](https://mlsanity.com/paper/1411.4280/) (2014)
- [SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation](https://mlsanity.com/paper/2004.03696/) (2020)
- [Towards a glaucoma risk index based on simulated hemodynamics from fundus images](https://mlsanity.com/paper/1805.10273/) (2018)
- [RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network](https://mlsanity.com/paper/2101.00535/) (2021)
- [A Fully Convolutional Neural Network based Structured Prediction Approach Towards the Retinal Vessel Segmentation](https://mlsanity.com/paper/1611.02064/) (2016)
- [The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models](https://mlsanity.com/paper/2009.01907/) (2020)
- [FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading](https://mlsanity.com/paper/2205.01676/) (2022)
- [Learning to Address Intra-segment Misclassification in Retinal Imaging](https://mlsanity.com/paper/2104.12138/) (2021)
- [PVBM: A Python Vasculature Biomarker Toolbox Based On Retinal Blood Vessel Segmentation](https://mlsanity.com/paper/2208.00392/) (2022)
- [Lirot.ai: A Novel Platform for Crowd-Sourcing Retinal Image Segmentations](https://mlsanity.com/paper/2208.10100/) (2022)

## Built on by (most cited first, 6 of 6)

- [RRWNet: Recursive Refinement Network for effective retinal artery/vein segmentation and classification](https://mlsanity.com/paper/2402.03166/) (2024)
- [VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images](https://mlsanity.com/paper/2409.16016/) (2024)
- [GONet: A Generalizable Deep Learning Model for Glaucoma Detection](https://mlsanity.com/paper/2502.19514/) (2025)
- [Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling](https://mlsanity.com/paper/2503.01190/) (2025)
- [VessShape: Few-shot 2D blood vessel segmentation by leveraging shape priors from synthetic images](https://mlsanity.com/paper/2510.27646/) (2025)
- [retinalysis-vascx: An explainable software toolbox for the extraction of retinal vascular biomarkers](https://mlsanity.com/paper/2602.08580/) (2026)

## Abstract (arXiv)

The retina is the only part of the human body in which blood vessels can be accessed non-invasively using imaging techniques such as digital fundus images (DFI). The spatial distribution of the retinal microvasculature may change with cardiovascular diseases and thus the eyes may be regarded as a window to our hearts. Computerized segmentation of the retinal arterioles and venules (A/V) is essential for automated microvasculature analysis. Using active learning, we created a new DFI dataset containing 240 crowd-sourced manual A/V segmentations performed by fifteen medical students and reviewed by an ophthalmologist, and developed LUNet, a novel deep learning architecture for high resolution A/V segmentation. LUNet architecture includes a double dilated convolutional block that aims to enhance the receptive field of the model and reduce its parameter count. Furthermore, LUNet has a long tail that operates at high resolution to refine the segmentation. The custom loss function emphasizes the continuity of the blood vessels. LUNet is shown to significantly outperform two state-of-the-art segmentation algorithms on the local test set as well as on four external test sets simulating distribution shifts across ethnicity, comorbidities, and annotators. We make the newly created dataset open access (upon publication).