Retinal Vessel Segmentation Based on Global and Local Information Fusion
Aiming at the problem of low efficiency of U-Net network in capturing global contextual information in retinal vessel segmentation methods,a method of integrating global and local information is proposed,which extracts local detail features and global contextual features while extracting features,and effectively fuses the two.Firstly,a pyramid vision Transformer branch parallel to the original convolution layer is added to the U-Net network encoder to extract global context information.Secondly,BiFusion module is introduced at the bottom of the encoder to effectively integrate the local information extracted from the U-Net convolutional layer with the global information extracted from Transformer branch,so as to obtain more complete feature information.The experimental results on FIVES and OCTA-500 data sets showed that the DICE coefficients of the network reached 90.09%and 83.28%,respectively,and the retinal segmentation effect was significantly improved.
Global InformationLocal InformationNetworkBranchRetinal vascular segmentation
万方数据
维普
