Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness. Accurate retinal vessel segmentation plays an important role in disease progression and diagnosis of such vision-threatening diseases. To this end, we propose a Multi-resolution Contextual Network (MRC-Net) that addresses these issues by extracting multi-scale features to learn contextual dependencies between semantically different features and using bi-directional recurrent learning to model former-latter and latter-former dependencies. Another key idea is training in adversarial settings for foreground segmentation improvement through optimization of the region-based scores. This novel strategy boosts the performance of the segmentation network in terms of the Dice score (and correspondingly Jaccard index) while keeping the number of trainable parameters comparatively low. We have evaluated our method on three benchmark datasets, including DRIVE, STARE, and CHASE, demonstrating its superior performance as compared with competitive approaches elsewhere in the literature.

翻译：及时且经济的视网膜疾病计算机辅助诊断对于预防失明至关重要。精确的视网膜血管分割在疾病进展监测及此类致盲性眼病的诊断中发挥着重要作用。为此，我们提出了一种多分辨率上下文网络（MRC-Net），该网络通过提取多尺度特征来学习语义不同特征之间的上下文依赖关系，并采用双向循环学习建模前-后与后-前依赖关系。另一核心思想是在对抗训练框架下通过优化区域评分来提升前景分割效果。该新颖策略在保持可训练参数数量相对较少的同时，有效提升了分割网络的Dice分数（及对应的Jaccard指数）性能。我们在DRIVE、STARE和CHASE三个基准数据集上评估了该方法，结果表明其性能优于现有文献中的其他竞争性方法。