RESUMEN
The segmentation of retinal vessel takes a crucial part in computer-aided diagnosis of diseases and eye disorders. However, the insufficient segmentation of the capillary vessels and weak anti-noise interference ability make such task more difficult. To solve this problem, we proposed a multi-scale residual attention network (MRANet) which is based on U-Net network. Firstly, to collect useful information about the blood vessels more effectively, we proposed a multi-level feature fusion block (MLF block). Then, different weights of each fused feature are learned by using attention blocks, which can retain more useful feature information while reducing the interference of redundant features. Thirdly, multi-scale residual connection block (MSR block) is constructed, which can better extract the image features. Finally, we use the DropBlock layer in the network to reduce the network parameters and alleviate network overfitting. Experiments show that based on DRIVE, the accuracy rate and the AUC performance value of our network are 0.9698 and 0.9899 respectively, and based on CHASE_DB1 dataset, they are 0.9755 and 0.9893 respectively. Our network has a better segmentation effect compared with other methods, which can ensure the continuity and completeness of blood vessel segmentation.