基于并行U-Net模型的眼底微血管图像分割方法  

Fundus Microvascular Image Segmentation Method Based on Parallel U⁃Net Model

作　　者：刘新娟 韩旭 方二喜[1] Liu Xinjuan;Han Xu;Fang Erxi(School of Electronic and Information,Soochow University,Suzhou 215006,Jiangsu,China)

机构地区：[1]苏州大学电子信息学院,江苏苏州215006

出　　处：《中国激光》2024年第21期95-108,共14页Chinese Journal of Lasers

基　　金：国家重点研发计划(2022YFB325003)。

摘　　要：眼底血管是医学上唯一可以无创直接观察到的组织,眼底图像不仅能直接反映眼部疾病状况,在监测全身血管疾病上也具有一定的临床价值。在眼底图像的智能化医学诊断技术中,视网膜血管分割是一项基础任务。针对眼底图像中微血管对比度较低、边界不清、分割灵敏度不高的问题,本文设计了一种基于改进U-Net的并行网络微血管分割模型,该模型分为主网络和微血管特征提取辅助网络两部分。设计了一种形态学图像处理方法,以获取微血管标签,提升微血管特征提取能力。为了增加特征空间的上下文信息量,在主网络中引入了多尺度特征混洗融合模块,将微血管特征信息融合到主网络特征信息流中,以增强其特征表达,提升微血管分割灵敏度。基于公开数据集DRIVE、CHASE_DB1和STARE的评估结果表明,所提方法在眼底微血管分割上展现出了良好的性能,在上述三个数据集上的准确度指标分别达到了0.9710、0.9764和0.9768。Objective The fundus is the only part of the human body where arteries,veins,and capillaries can be directly observed.Information on the vascular structure of the retina plays an important role in the diagnosis of fundus diseases and exhibits a close relationship with systemic vascular diseases such as diabetes,hypertension,and cardiovascular and cerebrovascular diseases.The accurate segmentation of blood vessels in retinal images can aid in analyzing the geometric parameters of retinal blood vessels and consequently evaluating systemic diseases.Deep learning algorithms have strong adaptability and generalization and have been widely used in fundus retinal blood vessel segmentation in recent years.Digital image processing technology based on deep learning can extract blood vessels from fundus images more quickly;however,the contrast of fundus images is mostly low at the boundary of blood vessels and microvasculature,and the extraction error of blood vessels is large.In particular,the microvasculature,which is similar in color to the background and has a smaller diameter,renders it more difficult to extract less vascular areas from the background.To solve this problem,this study improves the classical medical-image semantic segmentation U-Net.To effectively extract the spatial context information of color fundus images,a multiscale feature mixing and fusion module is designed to alleviate the limitations of local feature extraction by the convolution kernel.Moreover,to solve the problem of low contrast of the microvessels in color fundus images,a microvessel feature extraction auxiliary network is designed to facilitate the network in learning more detailed microvessel information and improve the performance of the network's blood vessel segmentation.Methods A microvascular segmentation model of a parallel network based on U-Net(MPU-Net)was designed based on microvascular detail information loss and limitations of the convolution kernels.The U-Net network model was improved.First,the U-Net network was paralleled w

关 键 词：视网膜血管分割 微血管特征提取 深度学习 形态学处理 多尺度特征混洗融合 

分 类 号：Q-334[生物学]