MAGF-Net: A multiscale attention-guided fusion network for retinal vessel segmentation

Retinal fundus images contain plenty of morphological information, so it is particularly important to realize precise segmentation of the retinal vessels for clinical diagnosis. With the rapid development of deep convolutional neural networks (DCNNs), to replace earlier manual labeling methods and reduce the labor cost, DCNN-based automatic segmentation methods have been greatly developed. U-Net and its variant models have obtained superior performance, but segmentation tasks are still challenging for the following reasons: First, features from encoders and decoders are not sufficiently fused to retain more effective information. Second, the limited receptive field will also affect contextual information extraction. In addition, although the continuous pooling operations can speed up the segmentation network training efficiency, they also lose detailed information during the downsampling process. To address the above issues and precisely segment the vessel structures from fundus images, a multiscale attention-guided fusion network, called MAGF-Net, is presented for automatic retinal vessel segmentation. To capture multiscale contextual features, a multiscale attention (MSA) block is proposed to construct the backbone network. Furthermore, a feature enhancement (FE) block is also proposed and embedded in the bottleneck layer to acquire global multiscale contextual information. To take full advantage of the channel information from deep layers and the spatial information from shallow layers, an attention-guided fusion (AGF) block is designed to fuse features from different network layers. Moreover, a hybrid feature pooling (HFP) block is employed to preserve more information during the downsampling operation. To evaluate the segmentation performance of the proposed MAGF-Net, extensive segmentation experiments are conducted on three public datasets: the CHASE_DB1 set, the DRIVE set and the STARE set. The experimental results show that the proposed MAGF-Net can obtain remarkable segmentation performance compared with other advanced methods. In particular, the ability of the proposed MAGF-Net to segment thin blood vessels is significantly improved.