Algorithm for Retinal Vessel Segmentation based on Axial Transformer and Convolutional Neural Network

Abstract

The analysis of retinal vessel images plays a crucial role in the early diagnosis of various diseases. Existing retinal vascular segmentation algorithms rely only on convolutional neural networks (CNN) to carry out feature extraction. However, due to the local nature of the convolutional operation, the CNN-based algorithms are difficult to extract the global features of the picture and are insensitive to the position information of each feature, which leads to poor connectivity among the segmented retinal vessels and insufficient segmentation of fine vessels. Therefore, we propose a retinal vessel segmentation algorithm based on an axial transformer and CNN. In this algorithm, the axial transformer is used to encode the position of each feature of the image and extract their global features, and the CNN is used to extract the local features of the image. The experimental results on the CHASE DB1 dataset indicate that the proposed algorithm can effectively segment the fine vessels in retinal images and significantly improve the segmentation accuracy of retinal vessels.