J Shanghai Jiaotong Univ Sci ›› 2025, Vol. 30 ›› Issue (4): 668-682.doi: 10.1007/s12204-024-2748-6
收稿日期:
2023-10-30
接受日期:
2024-02-05
出版日期:
2025-07-31
发布日期:
2025-07-31
邵党国,杨元彪,马磊,易三莉
Received:
2023-10-30
Accepted:
2024-02-05
Online:
2025-07-31
Published:
2025-07-31
摘要: 眼底视网膜血管的分割对于诊断眼部疾病至关重要。视网膜血管图像分割通常受到类别不平衡和血管尺度变化大的影响。这最终导致分割血管的不完整和连续性较差。本研究中,提出了CT-MFENet来解决上述问题。首先,使用上下文Transformer(CT)来整合上下文特征信息,这有助于像素间的远程建模,从而解决了血管连续性不完整的问题。其次,使用多尺度密集残差模块代替传统的CNN,以解决模型遇到多尺度血管时局部特征提取能力不足的问题。在解码阶段,引入的局部-全局融合模块增强了局部血管信息,并减小了高低级别特征之间的语义间隙。为了解决视网膜图像中的类别不平衡问题,提出一种混合损失函数,增强了模型对拓扑结构的分割能力。在公开的DRIVE、CHASEDB1、STARE和IOSTAR数据集上进行了实验。实验结果表明,提出的CT-MFENet在性能上优于大多数现有方法,包括基模型U-Net。
中图分类号:
. CT-MFENet:基于全局-局部特征融合的用于视网膜血管分割的上下文Transformer和多尺度特征提取网络[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(4): 668-682.
Shao Dangguo, Yang Yuanbiao, Ma Lei, Yi Sanli. CT-MFENet: Context Transformer and Multi-Scale Feature Extraction Network via Global-Local Features Fusion for Retinal Vessels Segmentation[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(4): 668-682.
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