Computational methods for automated analysis of corneal nerve images: Lessons learned from retinal fundus image analysis

Abstract

Corneal and retinal imaging provide a descriptive view of the nerve and vessel structure present inside the human eye, in a non-invasive manner. This helps in ocular, or other, disease identification and diagnosis. However, analyzing these images is a laborious task and requires expertise in the field. Therefore, there is a dire need for process automation. Although a large body of literature is available for automated analysis of retinal images, research on corneal nerve image analysis has lagged due to several reasons. In this article, we cover the recent research trends in automated analysis of corneal and retinal images, highlighting the requirements for automation of corneal nerve image analysis, and the possible reasons impeding its research progress. We also present a comparative analysis of segmentation algorithms versus their processing power derived from the studies included in the survey. Due to the advancement in retinal image analysis and the implicit similarities in retinal and corneal images, we extract lessons from the former and suggest ways to apply them to the latter. This is presented as future research directions for automatic detection of neuropathy using corneal nerve images. We believe that this article will be extremely informative for computer scientists and medical professionals alike, as the former would be informed regarding the different research problems waiting to be addressed in the field, while the latter would be enlightened to what is required from them so as to facilitate computer scientists in their path towards finding effective solutions to the problems.

Keywords: Automated image analysis; Corneal confocal microscopy; Deep learning; Neuropathy; Retinal fundoscopy.