An improved U-Net method with High-resolution Feature Maps for Retinal Blood Vessel Segmentation

Abstract

The accurate segmentation of retinal blood vessels is of great significance for the diagnosis of diseases such as diabetes, hypertension, microaneurysms and arteriosclerosis. However, manual segmentation of retinal blood vessels is time-consuming and laborious. This paper proposes a convolutional network structure based on U-Net for retinal vessel segmentation. First, a new convolution block, which makes full use of shallow high-resolution feature maps to minimize the information loss caused by downsampling, is added to the network. Second, the network was downscaled. Particularly, this network application conducts downsampling twice to reduce the complexity of the network and the number of parameters during training. In addition, we retain the original short connection, which merges the feature information of the shallow and deep networks. Therefore, this network can capture the details of blood vessels more effectively. We tested the work on the DRIVE data set and evaluated the accuracy, sensitivity, specificity and AUC, which were 0.9552, 0.7603, 0.9839, and 0.9789, respectively. A comprehensive comparison between the proposed algorithm and the existing algorithms shows that the various indicators of the algorithm perform well.