通过多分辨率上下文网络和对抗学习实现视网膜血管分割
Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning
AI总结：
本文提出MRC-Net，融合多尺度上下文特征、双向循环建模与对抗训练，在DRIVE、STARE和CHASE上以较少参数提升视网膜血管分割性能。
摘要：
及时且负担得起的视网膜疾病计算机辅助诊断对于预防失明至关重要。准确的视网膜血管分割在此类威胁视力疾病的进展和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以学习语义不同特征之间的上下文依赖关系，并使用双向循环学习来建模从前到后以及从后到前的依赖关系，从而解决这些问题。另一个关键思想是在对抗设置中进行训练，通过优化基于区域的分数来改进前景分割。这一新策略提高了分割网络在Dice分数（以及相应的Jaccard指数）方面的性能，同时保持可训练参数数量相对较低。我们在三个基准数据集（包括DRIVE、STARE和CHASE）上评估了所提方法，结果表明，与文献中其他具有竞争力的方法相比，该方法具有优越的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
