RNA-Net: Residual Nonlocal Attention Network for Retinal Vessel Segmentation

Automatic segmentation of retinal vessels is an important step in fundoscopic image analysis. Recently, convolutional-neural-network-based methods have been widely explored in this vision task. However, the local fixed receptive field makes network unable to collect global information and adapt to scale variation of retinal vessels. In this paper, we propose a novel RNA-Net which can capture nonlocal context dependencies across the inputs and extract multi-scale features for segmentation task. Firstly, we build a Residual Nonlocal Attention (RNA) Module, which can guide the network to pay more attention to task-related regions of the whole feature map. Secondly, to better capture the morphological characteristics of natural blood vessels, Pyramid Pooling Module (PPM) is added to capture features at multiple scales. Experimental results on two public datasets DRIVE and STARE clearly demonstrate that our method outperforms the current state-of-the-art approaches.