Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks

TL;DR

This paper introduces a generative adversarial network-based method for retinal vessel segmentation that achieves state-of-the-art accuracy, effectively capturing fine vessels and reducing false positives in fundoscopic images.

Contribution

The paper presents a novel GAN-based approach for retinal vessel segmentation that outperforms existing methods in accuracy and precision.

Findings

Dice coefficient of 0.829 on DRIVE dataset

Dice coefficient of 0.834 on STARE dataset

State-of-the-art segmentation performance

Abstract

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.