A feature aggregation and feature fusion network for retinal vessel segmentation

Neural networks have achieved outstanding performance in retinal vessel segmentation. However, since its continuous upsampling and convolution operation in the decoding stage, the semantic information and class information of the high-level features are destroyed. To address these problems, we proposed a new feature aggregation and feature fusion network (FAF-Net). Firstly, we introduced a multi-scale feature aggregation (MFA) block, which adjusts the receptive fields to learn more multi-scale features information. Furthermore, a feature reuse and distribution (FRD) block is intended to preserve the multi-scale feature information of the image and reduce the background noises in the feature map. Finally, the attention feature fusion (AFF) block is employed to effectively reduce the information loss of high-level features and connect the encoding and decoding stages. This multi-path combination helps to learn better representations and more accurate vessel feature maps. We evaluate the network on three retinal image databases (DRIVE, CHASEDB1, STARE). The proposed network outperforms existing current state-of-the-art vessel segmentation methods. Comprehensive experiments prove that FAF-Net is suited to processing medical image segmentation with limited samples and complicated features.