Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks
This work addresses the challenge of accurate vessel segmentation for medical imaging applications, though it appears incremental as it builds on existing GAN methods for a known bottleneck.
The paper tackles the problem of retinal vessel segmentation in fundoscopic images, which is crucial for automatic disease detection, by using generative adversarial networks to generate precise vessel maps, achieving state-of-the-art dice coefficients of 0.829 on DRIVE and 0.834 on STARE datasets.
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.