基于改进U-Net的多流视网膜血管分割算法

针对传统U型网络特征冗余以及视网膜血管形态复杂、细血管分割困难等问题, 提出一种基于改进U-Net的多流视网膜血管分割算法. 算法包含两种特征流向, 分别是全局分割流与边缘特化流. 为了减少特征冗余, 全局分割流在部分卷积的基础上采用快速提取模块替代传统的U-Net卷积块, 构建了能够高效提取血管特征、加快算法推理速度的改进U-Net模型; 为了减少噪声干扰、提升细血管的分割精度, 边缘特化流利用形态学生成的边缘标注信息为指导, 采用多个边缘提取模块, 结合全局分割流的高级语义特征以及边缘注意力, 更具针对性的提取血管细节信息, 增强细血管的特征表达. 在DRIVE与STARE数据集上进行了算法的有效性测试, 敏感度分别为0.8415和0.8369, 准确率分别为0.9701和0.9718, AUC值分别为0.9877和0.9909, 整体性能优于现有算法.

Multi-flow Retinal Vessel Segmentation Algorithm Based on Improved U-Net

In addressing issues such as feature redundancy in traditional U-shaped networks and the complexity of retinal vascular morphology, as well as challenges in segmenting fine blood vessels, this study proposes a multi-flow retinal vascular segmentation algorithm based on improved U-Net. The algorithm incorporates two feature flows, a global segmentation flow and a boundary-specialized flow. To reduce feature redundancy, the global segmentation flow replaces the traditional U-Net convolution block with a fast extraction module based on partial convolution and constructs an improved U-Net model that can efficiently extract vascular features and accelerate algorithm inference speed. To minimize noise interference and enhance the segmentation accuracy of fine blood vessels, the boundary-specialized flow utilizes morphologically generated boundary annotations as guidance. Multiple boundary extraction modules, in combination with the high-level semantic features from the global segmentation flow and boundary attention, are employed to more selectively extract vascular details, thereby strengthening the feature representation of fine blood vessels. The effectiveness of the algorithm is evaluated on the DRIVE and STARE datasets, yielding sensitivity values of 0.841 5 and 0.836 9, accuracy values of 0.970 1 and 0.971 8, and AUC values of 0.987 7 and 0.990 9, respectively. The overall performance surpassed that of existing algorithms.