# Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1706.09318 · HTML: https://mlsanity.com/paper/1706.09318/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2017.

## Built on by (most cited first, 17 of 17)

- [QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation](https://mlsanity.com/paper/2203.08483/) (2022)
- [RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network](https://mlsanity.com/paper/2101.00535/) (2021)
- [The RETA Benchmark for Retinal Vascular Tree Analysis](https://mlsanity.com/paper/2111.11658/) (2021)
- [Spectral Normalization and Dual Contrastive Regularization for Image-to-Image Translation](https://mlsanity.com/paper/2304.11319/) (2023)
- [Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models](https://mlsanity.com/paper/2104.02107/) (2021)
- [Orientation and Context Entangled Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2207.11396/) (2022)
- [Learning to Address Intra-segment Misclassification in Retinal Imaging](https://mlsanity.com/paper/2104.12138/) (2021)
- [PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training](https://mlsanity.com/paper/2207.11683/) (2022)
- [Retinal Vessel Segmentation with Deep Graph and Capsule Reasoning](https://mlsanity.com/paper/2409.11508/) (2024)
- [CathAI: Fully Automated Interpretation of Coronary Angiograms Using Neural Networks](https://mlsanity.com/paper/2106.07708/) (2021)
- [Progressive GANomaly: Anomaly detection with progressively growing GANs](https://mlsanity.com/paper/2206.03876/) (2022)
- [MLP-GAN for Brain Vessel Image Segmentation](https://mlsanity.com/paper/2207.08265/) (2022)
- [Revisiting Generative Adversarial Networks for Binary Semantic Segmentation on Imbalanced Datasets](https://mlsanity.com/paper/2402.02245/) (2024)
- [Enhancing Retinal Vascular Structure Segmentation in Images With a Novel Design Two-Path Interactive Fusion Module Model](https://mlsanity.com/paper/2403.01362/) (2024)
- [TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors](https://mlsanity.com/paper/2102.02690/) (2021)
- [Unsupervised Domain Adaptation for Retinal Vessel Segmentation with Adversarial Learning and Transfer Normalization](https://mlsanity.com/paper/2108.01821/) (2021)
- [Deep Learning Methods for Retinal Blood Vessel Segmentation: Evaluation on Images with Retinopathy of Prematurity](https://mlsanity.com/paper/2306.11576/) (2023)

## Abstract (arXiv)

Retinal vessel segmentation is an indispensable step for automatic detection of retinal diseases with fundoscopic images. Though many approaches have been proposed, existing methods tend to miss fine vessels or allow false positives at terminal branches. Let alone under-segmentation, over-segmentation is also problematic when quantitative studies need to measure the precise width of vessels. In this paper, we present a method that generates the precise map of retinal vessels using generative adversarial training. Our methods achieve dice coefficient of 0.829 on DRIVE dataset and 0.834 on STARE dataset which is the state-of-the-art performance on both datasets.