Deep learning in retinal optical coherence tomography (OCT): A comprehensive survey

Abstract

Retinal optical coherence tomography (OCT) images provide fundamental information regarding the health of the posterior eye (e.g., the retina and choroid). Thus, the development of automatic image analysis methods (e.g., segmentation) is fundamental to provide clinicians and researchers with quantitative data that facilitates decision making. In recent years, various machine learning (ML) methods have been developed to perform these automated image analyses, improving performance over traditional methods, increasing repeatability, and reducing the use of time-consuming manual analysis. Deep learning (DL), a sub-field of ML, represents a new approach that can improve image processing outcomes. Results published to date demonstrate that DL architectures generally achieve superior performance to previously proposed methods based on traditional image analysis or early ML techniques. Thus, DL methods for OCT image analysis have provided an important advance in the area of retinal layer segmentation in images from healthy eyes as well as those with pathologies. This paper provides a comprehensive narrative literature review of current DL layer segmentation methods applied to OCT images of the posterior segment of the eye. The manuscript provides an overview on the state-of-the-art in deep learning as well as highlighting some important areas for future developments to extend the analysis methods in this field.