Multi-Scale Residual U-Net Fundus Blood Vessel Segmentation Based on Attention Mechanism

Some existing retinal vessel segmentation methods have been unsuccessful in distinguishing weak blood vessels and have suffered from blood vessel segmentation disconnections at intersections. To solve this problem, a multi-scale U-shaped network based on attention mechanism was proposed in this paper. In the encoding part, the proposed algorithm employed the improved residual block structure to extract the depth features of blood vessels while effectively solving the overfitting problem. In turn, the multi-scale convolution module and multi-scale attention module were used to obtain multi-scale feature information of the depth features. Then, MaxBlurPool was used as the pooling method to reduce dimensions of data and ensure the translation invariance. In addition, hybrid attention module and parallel dilated convolution were presented in the last encoding layer, where the former emphasized the information that needs to he focused from the channel and space dimensions to suppress the interference of the background area and the latter was used to obtain the characteristic information of receptive fields with different sizes while not introducing redundant parameters to cause computational burden. In the decoding part, skip connection was improved to suppress noise and obtain more abundant context information. The proposed algorithm achieved better segmentation effect than other methods on public fundus datasets.