An effective segmentation method for retinal blood vessel image

This paper proposes an improved UNet segmentation algorithm to further improve the segmentation performance and adaptability to meet problems such as complex retinal blood vessel structures, low image contrast, and inaccurate segmentation of detail areas. To achieve this goal, two main strategies are adopted which are the residual module introducing depthwise separable convolution and the SE (Squeeze and Excitation) attention mechanism. First, a new residual module is designed by combining depthwise separable convolutional network and a residual network to replace the traditional convolution operation in the original UNet. This module not only improves the network's feature learning and expression capabilities but also enhances its ability to capture details and feature changes in images. Second, the SE attention mechanism is introduced to adaptively adjust the weights according to the importance of the channels in the feature map, allowing the network to focus more on channels containing important feature information. The experimental results show that compared to retinal blood vessel segmentation algorithms in recent years, the algorithm proposed in this paper performs better in performance.