电子科技大学学报2024，Vol.53Issue(3)：404-413,10.DOI:10.12178/1001-0548.2023131
面向视网膜血管精细分割的多层级图卷积特征融合神经编解码网络
Multilayer Graph Convolutional Feature Fusion Neural Encoding and Decoding Network for Fine Segmentation of Retinal Vessels
摘要
Abstract
Fundus retinal vessels segmentation can assist doctors in the diagnosis of ophthalmic diseases and cardiovascular and cerebrovascular diseases.However,due to the complex topological structure of blood vessels and unclear boundaries,it greatly increases the difficulty of segmentation.A graph convolutional feature fusion network is proposed based on the U-shaped structure to address these issues.This network uses a graph convolution module to model the global contextual information between pixels in encoder features,making up for the lack of global modeling ability in ordinary convolutions.Then,a multi-scale feature fusion module is used to fuse the encoder features and decoder features to reduce the impact of noise information in the feature layer on the segmentation results.Finally,a multi-level feature fusion module is used to fuse and output the features of each layer of the decoder,reducing the loss of spatial information and the reuse of deep features during the downsampling process.Verified on the public datasets DRIVE,CHASEDB1,and START,the F1 values and the AUC values are better than the other two methods.关键词
医学图像分割/视网膜血管/U型结构/图卷积/特征融合Key words
medical image segmentation/retinal vessels/U-shaped architecture/graph convolution/feature fusion分类
信息技术与安全科学引用本文复制引用
崔少国,张乐迁,文浩..面向视网膜血管精细分割的多层级图卷积特征融合神经编解码网络[J].电子科技大学学报,2024,53(3):404-413,10.基金项目
国家自然科学基金(62003065) （62003065）
重庆市科技局自然基金面上项目(CSTB2022NSCQ-MSX1206) （CSTB2022NSCQ-MSX1206）
重庆市技术预见与制度创新项目(CSTB2022TFII-OFX0042) （CSTB2022TFII-OFX0042）
教育部人文社科规划基金(22YJA870005) （22YJA870005）
重庆市教委重点项目(KJZD-K202200510) （KJZD-K202200510）
重庆市社会科学规划项目(2022NDYB119) （2022NDYB119）
重庆市教委人文社科项目(23SKGH072) （23SKGH072）
重庆师范大学人才基金(20XLB004) （20XLB004）
