  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.
