Automatic analysis of retinal blood images is of vital importance in
diagnosis tasks of retinopathy. Segmenting vessels accurately is a fundamental
step in analysing retinal images. However, it is usually difficult due to
various imaging conditions, low image contrast and the appearance of
pathologies such as micro-aneurysms. In this paper, we propose a novel method
with deep neural networks to solve this problem. We utilize U-net with residual
connection to detect vessels. To achieve better accuracy, we introduce an
edge-aware mechanism, in which we convert the original task into a multi-class
task by adding additional labels on boundary areas. In this way, the network
will pay more attention to the boundary areas of vessels and achieve a better
performance, especially in tiny vessels detecting. Besides, side output layers
are applied in order to give deep supervision and therefore help convergence.
We train and evaluate our model on three databases: DRIVE, STARE, and CHASEDB1.
Experimental results show that our method has a comparable performance with AUC
of 97.99% on DRIVE and an efficient running time compared to the
state-of-the-art methods.

该研究提出了一种基于深度学习的新方法，通过 U-net 神经网络和边缘感知机制实现视网膜血管图像的血管分割，实验结果表明该方法在三个数据库上的表现可与现有的最先进方法相媲美，具有 97.99％的 AUC，并且运行时间更有效率。