基于大核卷积残差块和坐标注意力的血管分割算法

Vessel Segmentation Algorithm Based on Large Kernel Convolution Residual Block and Coordinate Attention

摘要: 随着机器学习和深度学习的发展，研究者们应用各种算法和模型成功地从眼底图像中检测出相关疾病，其中视网膜血管的精确分割是眼科疾病自动化辅助诊断的关键步骤。然而传统分割算法存在血管与噪声难以区分，以及长距离血管拓扑结构难以保持连贯性等问题。为此，本文提出了一种基于大核卷积残差块和坐标注意力的眼底血管分割算法TLadder-AMSF。该算法采用具有大感受野的大核卷积残差块以建模长距离血管依赖，设计非对称多尺度融合模块AMSF以实现多尺度特征的精准聚合，并引入坐标注意力以增强空间方向感知能力。在DRIVE和STARE数据集上的实验结果表明，该方法在灵敏度上分别达到82.64%和83.56%，在准确率上分别达到96.25%和98.22%，各项指标与多个主流方法相比均达到先进水平。为了直观验证本文方法的分割性能，进行了可视化讨论。此外，消融实验证明大核卷积残差块、AMSF模块及坐标注意力提升了分割图像的拓扑连通性，验证了各个模块的有效性。

Abstract: With the development of machine learning and deep learning, researchers have successfully applied various algorithms and models to detect related diseases from fundus images, among which the precise segmentation of retinal blood vessels is a key step in automated ophthalmic disease diagnosis. However, traditional segmentation algorithms face issues such as difficulty distinguishing vessels from noise and maintaining the continuity of long-distance vessel topology. To address this, this paper proposes a fundus vessel segmentation algorithm TLadder-AMSF based on a large kernel convolution residual block and coordinate attention. This algorithm uses a Large Kernel Convolution Residual Block with a large receptive field to model long-distance vessel dependencies, designs an asymmetric multi-scale fusion module AMSF to achieve accurate aggregation of multi-scale features, and introduces a coordinate attention to enhance spatial directional perception. Experimental results on the DRIVE and STARE datasets show that the method achieves sensitivities of 82.64% and 83.56%, and accuracies of 96.25% and 98.22%, respectively, with all metrics reaching advanced levels compared to several mainstream methods. To visually validate the segmentation performance of this method, visualization discussions were conducted. In addition, ablation experiments demonstrated that the Large Kernel Convolution Residual Block, AMSF module, and coordinate attention improved the topological connectivity of segmented images, confirming the effectiveness of each module.