SAT-Net: a side attention network for retinal image segmentation

Abstract

Retinal vessel segmentation plays an important role in the automatic assessment of eye health. Deep learning technology has been extensively employed in medical image segmentation. Specifically, U-net based methods achieve great success in medical image segmentation. However, due to its continuous pooling layer and convolution operation, the spatial information and texture information of the image are destroyed. To address this issue, we propose a SAT-Net that integrates side attention and dense atrous convolution block, which also consists of multi-scale input so that the network can retain more features of the image of the encoder stage. The dense atrous convolution block enables multiple receptive field fusion, which preserves the context information of the image, and the side attention mechanism further enhances the high-level information of the encoded features and reduces the noise in the feature map. We apply this method to different retinal image segmentation datasets and compare with the other methods. The experimental results demonstrate the effectiveness of the proposed method.