文献类型：期刊文献

中文题名：一种基于CNN-Transformer的青光眼辅助诊断双编码分割网络模型

英文题名：CNN-Transformer-based dual-encoder segmentation network model for glaucoma auxiliary diagnosis

作者：马宇张[1];张伟[1];邵浩辰[1]

第一作者：马宇张

机构：[1]甘肃中医药大学医学信息工程学院,甘肃兰州730000

第一机构：甘肃中医药大学信息工程学院（教育技术中心）

年份：2026

卷号：43

期号：2

起止页码：268

中文期刊名：中国医学物理学杂志

外文期刊名：Chinese Journal of Medical Physics

基金：甘肃省教育厅创新基金(2022B-113)。

语种：中文

中文关键词：视杯视盘分割;青光眼辅助诊断;Transformer;特征融合;注意力机制

外文关键词：optic cup and disc segmentation;auxiliary diagnosis of glaucoma;Transformer;feature fusion;attention mechanism

摘要：视杯与视盘的精准分割是青光眼早期筛查中形态学参数计算的关键环节,针对现有方法因局部-全局特征融合效率低、长距离依赖建模不足导致的边界模糊和分割精度受限问题,提出一种基于CNN-Transformer的青光眼辅助诊断双编码分割网络模型。首先,设计双分支互补特征融合模块替代传统跳跃连接,通过动态权重分配策略实现CNN局部细节与Transformer全局上下文的协同优化以提升特征融合效率。其次,在Transformer编码器中引入全局注意力增强模块,利用多头自注意力机制建模像素级长距离依赖关系,结合深度可分离卷积增强边界区域的上下文感知能力,有效缓解视杯/视盘边缘不连续问题。在REFUGE数据集上的实验表明,该方法在视盘分割任务中Dice系数和IoU较U-Net分别提升4.11%和5.62%;该方法在视杯分割任务中Dice系数和IoU较U-Net分别提升11.75%和19.30%。
Accurate segmentation of the optic cup and optic disc is a critical step for calculating morphological parameters in the early screening of glaucoma.To address the issues of boundary blurring and limited segmentation accuracy caused by the inefficiency of local-global feature fusion and inadequate modeling of long-distance dependency modeling in existing methods,this study proposes a dual-encoder segmentation network model based on CNN-Transformer for auxiliary diagnosis of glaucoma.Specifically,a dual-branch complementary feature fusion module is designed to replace the traditional skip connection,and a dynamic weight allocation strategy is adopted to achieve the synergistic optimization of the local details of the CNN and the global context of the Transformer,thereby improving the efficiency of feature fusion.Furthermore,a global attention enhancement module is introduced into the Transformer encoder,which models the pixel-level long-distance dependencies using the multi-head self-attention mechanism,and enhances the context awareness of the boundary regions by integrating the depth-separable convolution,thus effectively alleviating the discontinuity of optic cup/disc edge.Experiments on the REFUGE dataset show that compared with U-Net,the proposed method achieves a 4.11%improvement in Dice coefficient and 5.62%improvement in IoU for the optic disc segmentation task,while for the optic cup segmentation task,the corresponding improvements in the Dice coefficient and IoU reach 11.75%and 19.30%,respectively.