# The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models

arXiv: https://arxiv.org/abs/2009.01907 · HTML: https://mlsanity.com/paper/2009.01907/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [Do We Really Need Dice? The Hidden Region-Size Biases of Segmentation Losses](https://mlsanity.com/paper/2104.08717/) (2021)
- [LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images](https://mlsanity.com/paper/2309.05780/) (2023)
- [VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images](https://mlsanity.com/paper/2409.16016/) (2024)
- [Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization](https://mlsanity.com/paper/2406.14994/) (2024)
- [Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity](https://mlsanity.com/paper/2302.02524/) (2023)
- [Polyp and Surgical Instrument Segmentation with Double Encoder-Decoder Networks](https://mlsanity.com/paper/2406.03901/) (2024)
- [Uncertainty-Aware Retinal Vessel Segmentation via Ensemble Distillation](https://mlsanity.com/paper/2509.11689/) (2025)
- [Artery-Vein Segmentation from Fundus Images using Deep Learning](https://mlsanity.com/paper/2510.03717/) (2025)
- [Robust Multi-Disease Retinal Classification via Xception-Based Transfer Learning and W-Net Vessel Segmentation](https://mlsanity.com/paper/2512.10608/) (2025)
- [retinalysis-vascx: An explainable software toolbox for the extraction of retinal vascular biomarkers](https://mlsanity.com/paper/2602.08580/) (2026)

## Abstract (arXiv)

The segmentation of the retinal vasculature from eye fundus images represents one of the most fundamental tasks in retinal image analysis. Over recent years, increasingly complex approaches based on sophisticated Convolutional Neural Network architectures have been slowly pushing performance on well-established benchmark datasets. In this paper, we take a step back and analyze the real need of such complexity. Specifically, we demonstrate that a minimalistic version of a standard U-Net with several orders of magnitude less parameters, carefully trained and rigorously evaluated, closely approximates the performance of current best techniques. In addition, we propose a simple extension, dubbed W-Net, which reaches outstanding performance on several popular datasets, still using orders of magnitude less learnable weights than any previously published approach. Furthermore, we provide the most comprehensive cross-dataset performance analysis to date, involving up to 10 different databases. Our analysis demonstrates that the retinal vessel segmentation problem is far from solved when considering test images that differ substantially from the training data, and that this task represents an ideal scenario for the exploration of domain adaptation techniques. In this context, we experiment with a simple self-labeling strategy that allows us to moderately enhance cross-dataset performance, indicating that there is still much room for improvement in this area. Finally, we also test our approach on the Artery/Vein segmentation problem, where we again achieve results well-aligned with the state-of-the-art, at a fraction of the model complexity in recent literature. All the code to reproduce the results in this paper is released.