# RRWNet: Recursive Refinement Network for effective retinal artery/vein segmentation and classification

arXiv: https://arxiv.org/abs/2402.03166 · HTML: https://mlsanity.com/paper/2402.03166/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 81 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [DUNet: A deformable network for retinal vessel segmentation](https://mlsanity.com/paper/1811.01206/) (2018)
- [Beyond the Pixel-Wise Loss for Topology-Aware Delineation](https://mlsanity.com/paper/1712.02190/) (2017)
- [Towards a glaucoma risk index based on simulated hemodynamics from fundus images](https://mlsanity.com/paper/1805.10273/) (2018)
- [Retinal vessel segmentation based on Fully Convolutional Neural Networks](https://mlsanity.com/paper/1812.07110/) (2018)
- [Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification](https://mlsanity.com/paper/2007.09337/) (2020)
- [A Fully Convolutional Neural Network based Structured Prediction Approach Towards the Retinal Vessel Segmentation](https://mlsanity.com/paper/1611.02064/) (2016)
- [RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging](https://mlsanity.com/paper/2203.14928/) (2022)
- [Joint segmentation and classification of retinal arteries/veins from fundus images](https://mlsanity.com/paper/1903.01330/) (2019)
- [Multimodal Transfer Learning-based Approaches for Retinal Vascular Segmentation](https://mlsanity.com/paper/2012.10160/) (2020)
- [TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification](https://mlsanity.com/paper/2007.14852/) (2020)
- [LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images](https://mlsanity.com/paper/2309.05780/) (2023)
- [Simultaneous segmentation and classification of the retinal arteries and veins from color fundus images](https://mlsanity.com/paper/2209.09582/) (2022)
- [Flood-Filling Networks](https://mlsanity.com/paper/1611.00421/) (2016)
- [Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection](https://mlsanity.com/paper/1703.08493/) (2017)

## Built on by (most cited first, 4 of 4)

- [MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis](https://mlsanity.com/paper/2506.08900/) (2025)
- [EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors](https://mlsanity.com/paper/2606.20108/) (2026)
- [Improving segmentation of retinal arteries and veins using cardiac signal in doppler holograms](https://mlsanity.com/paper/2511.14654/) (2025)
- [MARVEL: Universal Murray's Law-informed Vessel Tree Segmentation and Topology Estimation](https://mlsanity.com/paper/2605.25363/) (2026)

## Abstract (arXiv)

The caliber and configuration of retinal blood vessels serve as important biomarkers for various diseases and medical conditions. A thorough analysis of the retinal vasculature requires the segmentation of the blood vessels and their classification into arteries and veins, typically performed on color fundus images obtained by retinography. However, manually performing these tasks is labor-intensive and prone to human error. While several automated methods have been proposed to address this task, the current state of art faces challenges due to manifest classification errors affecting the topological consistency of segmentation maps. In this work, we introduce RRWNet, a novel end-to-end deep learning framework that addresses this limitation. The framework consists of a fully convolutional neural network that recursively refines semantic segmentation maps, correcting manifest classification errors and thus improving topological consistency. In particular, RRWNet is composed of two specialized subnetworks: a Base subnetwork that generates base segmentation maps from the input images, and a Recursive Refinement subnetwork that iteratively and recursively improves these maps. Evaluation on three different public datasets demonstrates the state-of-the-art performance of the proposed method, yielding more topologically consistent segmentation maps with fewer manifest classification errors than existing approaches. In addition, the Recursive Refinement module within RRWNet proves effective in post-processing segmentation maps from other methods, further demonstrating its potential. The model code, weights, and predictions will be publicly available at https://github.com/j-morano/rrwnet.