西安电子科技大学学报（自然科学版）2025，Vol.52Issue(2)：85-100,16.DOI:10.19665/j.issn1001-2400.20241204
动态图谱融合Transformer视网膜血管分割算法
Dynamic graph reasoning transformer retinal vessel segmentation algorithm
摘要
Abstract
To address the issues of excessive loss of vascular features at the encoding end,poor segmentation ability of vascular regions in lesion areas,and insufficient extraction of global contextual information in existing algorithms,this paper proposes a dynamic feature weighting and graph reasoning Transformer retinal vessel segmentation algorithm.First,an adaptive weighting encoding end is designed to alleviate the problem of vascular loss caused by continuous convolution and downsampling,thus enhancing vascular texture features.Second,a graph reasoning Transformer module is constructed to simultaneously extract pixel-level vascular features and relationships between nodes,thereby effectively capturing global and local information in image data.The final construction of the dynamic feature enhancement module at the decoder side and the encoder-decoder base effectively improves the ability to segment vascular lesions.Experimental results on DRIVE,CHASE-DB1 and STARE datasets show that the proposed algorithm exhibits a superior segmentation performance and generalization ability with only 0.91M model parameters,with accuracies of 97.01％,97.37％,and 97.42％,sensitivities of 82.51％,84.47％,and 81.21％,and AUC-ROC of 98.74％,98.83％,and 98.94％,respectively,showing a certain clinical application value in the diagnosis of ophthalmic diseases.关键词
视网膜血管分割/图卷积/Transformer/自适应加权下采样/动态特征增强Key words
retinal vessel segmentation/graph convolution/Transformer/adaptive weighted downsampling/dynamic feature enhancement分类
计算机与自动化引用本文复制引用
梁礼明,卢宝贺,龙鹏威,金家新,吴健..动态图谱融合Transformer视网膜血管分割算法[J].西安电子科技大学学报（自然科学版）,2025,52(2):85-100,16.基金项目
国家自然科学基金(51365017,61463018) （51365017,61463018）
江西省自然科学基金(20192BAB205084) （20192BAB205084）
江西省教育厅科学技术研究青年项目(GJJ2200848) （GJJ2200848）
