ARA-GAN: Adaptive Residual Attention Generative Adversarial Network for Retinal Vessel Segmentation

Abstract

Automatic segmentation of retinal vessels is a critical task in fundoscopic image analysis. The emergence of deep learning has shown promising abilities of feature representation, particularly with Convolutional Neural Networks (CNNs). However, the fixed receptive field in CNNs limits their ability to adapt to the scale variation of natural vascular networks and capture nonlocal context dependencies across feature maps. To address these limitations, we propose a novel model called ARA-GAN that can adaptively extract nonlocal feature contexts and aggregate multi-scale information for retinal vessel segmentation. The proposed model comprises a novel Generative Adversarial Network (GAN) as the overall framework to obtain global information and strong robustness. Additionally, we integrate Residual Nonlocal Attention (RNA) Module into the framework to adaptively capture nonlocal context dependencies across the input features. Finally, we add a Pyramid Pooling Module (PPM) to extract the morphological characteristics of natural retinal vessels at multiple scales. Our experimental results demonstrate that our method outperforms state-of-the-art approaches on both the DRIVE and STARE datasets.