Application of Improved U-Net in Retinal Vessel Segmentation

Abstract

In order to solve the problems that it is difficult to accurately identify the vascular boundary and the low contrast between the blood vessel and the background in fundus retinal vascular segmentation, an encoder-decoder algorithm is proposed. In order to improve the segmentation ability of the algorithm at the vascular boundary, the global convolutional network (GCN) and boundary refinement (BR) are used to replace the traditional convolution layer in the coding part, and the improved position attention (PA) module and channel attention (CA) module are introduced in the jump connection part. The aim is to increase the contrast between the blood vessels and the background, so that the network can better separate the blood vessels from the background. In addition, in order to improve the performance of the network, the dense convolution network is used in the last layer of the coding part to solve the problem of network overfitting, and in order to solve the problem of gradient explosion and gradient disappearance to a certain extent, in each layer of the decoding part, the convolution long-short memory network is used to improve the ability of the network to obtain feature information. Tested on the common datasets DRIVE and CHASE_DB1, the sensitivity, specificity, accuracy, F1-Score and AUC are used as evaluation indicators, in which the accuracy and AUC reach 96.99%, 98.77% and 97.51%, 99.01%, respectively. This algorithm can effectively improve the accuracy of blood vessel segmentation in fundus image.

Keywords

• |retinal vessels|u-net|boundary refinement (br)|position attention (pa) module|channel attention (ca) module|global convolutional network (gcn)