Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks

2017/06/28 by Jaemin Son, Son, Jaemin, Sang Jun Park +3
Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #Retinal Imaging and Analysis

paper · pdf · doi:10.48550/arxiv.1706.09318

openalex publication_date 2017/06/28 · openalex created_date 2017/07/14 · openalex updated_date 2026/07/28

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.

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