计算机工程与科学2023，Vol.45Issue(12)：2175-2185,11.DOI:10.3969/j.issn.1007-130X.2023.12.009
基于双解码器结构的多尺度注意力特征融合网络的视网膜血管分割
Retinal vessel segmentation based on multi-scale attention feature fusion network with dual-decoder structure
摘要
Abstract
To solve the problem of irregular and difficult segmentation of blood vessels in fundus ret-inal images,a multi-scale attention feature fusion network model based on a dual-decoder structure is proposed to achieve accurate segmentation of retinal blood vessels.The dual decoder branch network structure can reduce information loss.In the encoder,the multi-scale attention feature fusion module is designed to extract rich multi-scale features and the spatial attention module is combined to enhance the extraction of spatial context information and improve vascular recognition ability.Squeeze-and-excitation module is used to optimize aggregated features,suppress irrelevant feature channels and improve the comprehensive segmentation ability of the model.The experimental results on the DRIVE and CHASEDB1 data sets show that the recall rate can reach 0.841 1 and 0.855 1 respectively,making great progress compared with some advanced networks at present,with the maximum increase of 6.6%and 8.25%respectively.关键词
医学图像分割/视网膜血管分割/双解码器结构/多尺度特征提取/空间注意力模块Key words
medical image segmentation/retinal vessel segmentation/dual-decoder structure/multi-scale feature extraction/spatial attention module分类
信息技术与安全科学引用本文复制引用
张文豪,瞿绍军..基于双解码器结构的多尺度注意力特征融合网络的视网膜血管分割[J].计算机工程与科学,2023,45(12):2175-2185,11.基金项目
国家自然科学基金(12071126) （12071126）
湖南省教育厅科学研究重点项目(23A0081) （23A0081）
