CGAN-Based Lacquer Cracks Segmentation in ICGA Image

Abstract

The increasing prevalence of high myopia has raised concern worldwide. In high myopia, myopia macular degeneration (MMD) is a major cause of vision impairment and lacquer crack (LC) is one of the main signs of MMD. Since the development of LC can reflect the severity of MMD, it is important and meaningful to segment LCs. Indocyanine green angiography (ICGA) has been used for visualizing LCs and is considered to be superior to fluorescein angiography (FA). However, LCs segmentation is difficult due to the image blurring and the confusion between LCs and the background. In this paper, we propose an automatic LCs segmentation method based on the improved conditional generative adversarial nets (cGAN). To apply the advanced cGAN on ICGA images, Dice loss function is added to improve the accuracy of segmentation. Experiments on the ICGA images of high myopia denoted that the proposed method can successfully segment LCs with the trained model and achieve better performance than other popular nets.