Cascaded Attention Guided Network for Retinal Vessel Segmentation

Segmentation of retinal vessels is of great importance in the diagnosis of eye-related diseases. Many learning-based methods have been proposed for this task and get encouraging results. In this paper, we propose a novel end-to-end Cascaded Attention Guided Network (CAG-Net) for retinal vessel segmentation, which can generate more accurate results for retinal vessel segmentation. Our CAG-Net is a two-step deep neural network which contains two modules, the prediction module and the refinement module. The prediction module is responsible for generating an initial segmentation map, while the refinement module aims at improving the initial segmentation map. The final segmentation result is obtained by integrating the outputs of the two modules. Both of the two modules adopt an Attention UNet++ (AU-Net++) to boost the performance, which employs Attention guided Convolutional blocks (AC blocks) on the decoder. The experimental results show that our proposed network achieved state-of-the-art performance on the three public retinal datasets DRIVE, CHASE DB1 and STARE.