Development of automatic retinal vessel segmentation method in fundus images via convolutional neural networks.

Abstract

The analysis of fundus photograph is one of useful diagnosis tools for diverse retinal diseases such as diabetic retinopathy and hypertensive retinopathy. Specifically, the morphology of retinal vessels in patients is used as a measure of classification in retinal diseases and the automatic processing of fundus image has been investigated widely for diagnostic efficiency. The automatic segmentation of retinal vessels is essential and needs to precede computer-aided diagnosis system. In this study, we propose the method which implements patch-based pixel-wise segmentation with convolutional neural networks (CNNs) in fundus images for automatic retinal vessel segmentation. We construct the network composed of several modules which include convolutional layers and upsampling layers. Feature maps are made by modules and concatenated into a single feature map to capture coarse and fine structures of vessel simultaneously. The concatenated feature map is followed by a convolutional layer for performing a pixel-wise prediction. The performance of the proposed method is measured on DRIVE dataset. We show that our method is comparable to the results of other state-of-the-art algorithms.