Overview of Deep Learning Methods for Retinal Vessel Segmentation
It provides a survey for researchers in medical imaging, but it is incremental as it only summarizes existing work.
This paper reviews recent deep learning methods for retinal vessel segmentation, analyzing their design, performance metrics, and pros and cons without presenting new results.
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.