论文标题
通过递归语义引导的网络提高视网膜血管分割中的连通性
Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network
论文作者
论文摘要
已经提出了许多基于深度学习的方法用于视网膜血管分割，但是很少有人专注于分段容器的连通性，这对于视网膜图像上实用的计算机辅助诊断系统非常重要。在本文中，我们提出了一个有效的网络来解决此问题。通过引入语义引导的模块来增强U形网络，该模块将丰富的语义信息集成到浅层层中，以指导网络探索更强大的功能。此外，递归精致迭代在先前的分割结果上应用了相同的网络，以逐步提高性能，同时没有增加额外的网络参数。精心设计的递归语义引导网络已在几个公共数据集上进行了广泛的评估。实验结果表明该方法的效率。
Many deep learning based methods have been proposed for retinal vessel segmentation, however few of them focus on the connectivity of segmented vessels, which is quite important for a practical computer-aided diagnosis system on retinal images. In this paper, we propose an efficient network to address this problem. A U-shape network is enhanced by introducing a semantics-guided module, which integrates the enriched semantics information to shallow layers for guiding the network to explore more powerful features. Besides, a recursive refinement iteratively applies the same network over the previous segmentation results for progressively boosting the performance while increasing no extra network parameters. The carefully designed recursive semantics-guided network has been extensively evaluated on several public datasets. Experimental results have shown the efficiency of the proposed method.
