  Methods for automated retinal vessel segmentation play an important role in
the treatment and diagnosis of many eye and systemic diseases. With the fast
development of deep learning methods, more and more retinal vessel segmentation
methods are implemented as deep neural networks. In this paper, we provide a
brief review of recent deep learning methods from highly influential journals
and conferences. The review objectives are: (1) to assess the design
characteristics of the latest methods, (2) to report and analyze quantitative
values of performance evaluation metrics, and (3) to analyze the advantages and
disadvantages of the recent solutions.
