改进的U-Net在视网膜血管分割上的应用

针对眼底视网膜血管分割中血管边界难以精确识别以及血管与背景对比度低而难以分割的问题,提出一种编码器-解码器结构的算法.为了提高算法在血管边界的分割能力,在编码部分采用全局卷积网络(GCN)和边界细化(BR)替换传统的卷积层;在跳跃连接部分引入改进的位置注意模块(PA)和通道注意模块(CA),目的是增加血管与背景之间的对...

Application of Improved U-Net in Retinal Vessel Segmentation

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.