A Generative Adversarial Framework for Capillary Non-perfusion Regions Segmentation in Fundus Fluorescein Angiograms

Retinal capillary non-perfusion (CNP) is one of diabetic retinal vascular diseases. As the capillaries are occluded, blood stops flowing to certain regions of the retina, resulting in the formation of non-perfused regions. Accurate determination of the area and change of CNP is of great significance in clinical judgment of the extent of vascular obstruction and selection of treatment methods. This paper proposes a novel generative adversarial framework, and realize the segmentation of non-perfusion regions in fundus fluorescein angiography images. The generator G of GANs is trained to produce "real" images; while an adversarially trained discriminator D is trained to do as well as possible at detecting "fakes" images from the generator. In this paper, a U-shaped network is used as the discriminator. Our method is validated using on 138 clinical fundus fluorescein angiography images. Experimental results show that our method achieves more accurate segmentation results than that of state-of-the-art approaches.