IEEE J Biomed Health Inform. 2021 Jul;25(7):2722-2732. doi: 10.1109/JBHI.2020.3044957. Epub 2021 Jul 27.
Retinal vessel segmentation and centerline extraction are crucial steps in building a computer-aided diagnosis system on retinal images. Previous works treat them as two isolated tasks, while ignoring their tight association. In this paper, we propose a deep semantics and multi-scaled cross-task aggregation network that takes advantage of the association to jointly improve their performances. Our network is featured by two sub-networks. The forepart is a deep semantics aggregation sub-network that aggregates strong semantic information to produce more powerful features for both tasks, and the tail is a multi-scaled cross-task aggregation sub-network that explores complementary information to refine the results. We evaluate the proposed method on three public databases, which are DRIVE, STARE and CHASE_DB1. Experimental results show that our method can not only simultaneously extract retinal vessels and their centerlines but also achieve the state-of-the-art performances on both tasks.
视网膜血管分割和中心线提取是构建视网膜图像计算机辅助诊断系统的关键步骤。以前的工作将它们视为两个孤立的任务，而忽略了它们之间的紧密联系。在本文中，我们提出了一种深度语义和多尺度跨任务聚合网络，利用这种关联来共同提高它们的性能。我们的网络有两个子网络。前半部分是一个深度语义聚合子网络，它聚合了强大的语义信息，为两个任务生成更强大的特征，后半部分是一个多尺度跨任务聚合子网络，它探索互补信息以细化结果。我们在三个公共数据库 DRIVE、STARE 和 CHASE_DB1 上评估了所提出的方法。实验结果表明，我们的方法不仅可以同时提取视网膜血管及其中心线，而且可以在这两个任务上都达到最先进的性能。
