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.

提出一种名为 MRC-Net 的多分辨率上下文网络，其中采用多尺度特征提取技术以学习语义不同特征之间的上下文依赖性，并使用双向递归学习模型前者 - 后者和后者 - 前者之间的依赖性，在对前景分割进行对抗训练的基础上进行训练，以提高分割网络的性能，同时保持可比较低的可训练参数数量。该方法在 DRIVE、STARE 和 CHASE 三个基准数据集上表现优异，与现有的竞争方法相比具有更好的性能。