U-GAN: Generative Adversarial Networks with U-Net for Retinal Vessel Segmentation

The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monitor them. However, existing methods have various problems in the segmentation of the retinal vessels, such as insufficient segmentation of retinal vessels, weak anti-noise interference ability. Aiming to the shortcomings of existed methods, this paper proposes an improved model based on the Generative Adversarial Networks with U-Net, which contains densely-connected convolutional network and a novel attention gate (AG) model in the generator, referred as U-GAN, to automatically segment the retinal blood vessels. The method can strengthen feature propagation, substantially reduce the number of parameters, and automatically learn to focus on target structures without additional supervision. By verifying the method on the DRIVE datasets, the segmentation accuracy rate is 96.15%, higher than that of U-Net and R2U-Net.