Retinal Vessel Segmentation Based on Attention Mechanism and U-Net

Retinal blood vessel segmentation is of great significance to assist doctors in clinical screening and diagnosis of large-scale diseases such as ophthalmology and diabetes. Aiming at the problems of insufficient segmentation of fine blood vessels and the influence of complex background regions in the existing retinal blood vessel segmentation algorithms, a TCU-Net based on attention mechanism and U-Net is proposed to segment retinal vessels. Firstly, use single-channel feature extraction, image cropping and other preprocessing methods to perform feature enhancement and data expansion on the original fundus image. Secondly, the TCU-Net is proposed based on the U-Net to segment the preprocessed image. The encoder combines ResNet and Transformer to capture the image details and global feature information, which can effectively enhance the ability of vessel feature extraction, the decoder introduces an ICA block with improved channel attention to assist the upsampling process to refine the segmentation results. The experimental results on the DRIVE and CHASEDB1 datasets show that the accuracy of the proposed algorithm is 0.9684 and 0.974 8, the sensitivity is 0.7899 and 0.8256, and the Area Under Curve is 0.9820 and 0.987 6, the comprehensive segmentation performance is better than U-Net, CIEU-Net and other algorithms.