Overview of Deep Learning Methods for Retinal Vessel Segmentation

TL;DR

This paper reviews recent deep learning techniques for retinal vessel segmentation, analyzing their design, performance metrics, and pros and cons to guide future research in medical image analysis.

Contribution

It provides a comprehensive overview of recent deep learning approaches, highlighting their design features and performance in retinal vessel segmentation.

Findings

Deep learning methods have significantly advanced retinal vessel segmentation.

Performance metrics vary across different models and datasets.

The review identifies strengths and limitations of current approaches.

Abstract

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.