基于U-Net的多尺度特征增强视网膜血管分割算法  

Multi-scale feature enhanced retinal vessel segmentation algorithm based on U-Net

作　　者：张志昂 廖光忠[2] ZHANG Zhiang;LIAO Guangzhong(School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan Hubei 430065,China;Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System(Wuhan University of Science and Technology),Wuhan Hubei 430065,China)

机构地区：[1]武汉科技大学计算机科学与技术学院,武汉430065 [2]智能信息处理与实时工业系统湖北省重点实验室(武汉科技大学),武汉430065

出　　处：《计算机应用》2023年第10期3275-3281,共7页journal of Computer Applications

基　　金：武汉市重点研发计划项目(2022012202015070)。

摘　　要：针对传统视网膜血管分割算法存在血管分割精度低和病灶区域误分割等缺点,提出一种基于U-Net的多尺度特征增强视网膜血管分割算法(MFEU-Net)。首先,为解决梯度消失问题,设计一种改进的特征信息增强残差模块(FIE-RM)替代U-Net的卷积块;其次,为扩大感受野并提高对血管信息特征的抽取能力,在U-Net的底部引入多尺度密集空洞卷积模块;最后,为减少编解码过程中的信息损失,在U-Net的跳跃连接处构建多尺度通道增强模块。在DRIVE(Digital Retinal Images for Vessel Extraction)和CHASE_DB1数据集上的实验结果表明,与在视网膜血管分割方面表现次优的算法CS-Net(Channel and Spatial attention Network)相比,MFEU-Net的F1分数分别提高了0.35和1.55个百分点,曲线下面积(AUC)分别提高了0.34和1.50个百分点,这验证了MFEU-Net可以有效提高对视网膜血管分割的准确性和鲁棒性。Aiming at the shortcomings of traditional retinal vessel segmentation algorithm such as low accuracy of vessel segmentation and mis-segmentation of focal areas,a Multi-scale Feature Enhanced retinal vessel segmentation algorithm based on U-Net(MFEU-Net)was proposed.Firstly,in order to solve the vanishing gradient problem,an improved Feature Information Enhancement Residual Module(FIE-RM)was designed to replace the convolution block of U-Net.Secondly,in order to enlarge the receptive field and improve the extraction ability of vascular information features,a multi-scale dense atrous convolution module was introduced at the bottom of U-Net.Finally,in order to reduce the information loss in the process of encoding and decoding,a multi-scale channel enhancement module was constructed at the skip connection of UNet.Experimental results on Digital Retinal Images for Vessel Extraction(DRIVE)and CHASE_DB1 datasets show that compared with CS-Net(Channel and Spatial attention Network),the suboptimal algorithm in retinal vessel segmentation,MFEU-Net has the F1 score improved by 0.35 and 1.55 percentage points respectively,and the Area Under Curve(AUC)improved by 0.34 and 1.50 percentage points respectively.It is verified that MFEU-Net can improve the accuracy and robustness of retinal vessel segmentation effectively.

关 键 词：视网膜血管分割 U-Net 多尺度信息 密集空洞卷积 残差网络 病灶区域 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]