The effect of optical degradation from cataract using a new Deep Learning optical coherence tomography segmentation algorithm

Abstract

Purpose: To assess the validity of the results of a freely available online Deep Learning segmentation tool and its sensitivity to noise introduced by cataract.

Methods: The OCT images were collected with a Spectralis SD-OCT (Heidelberg Engineering, Heidelberg, Germany) as part of normal clinical practice. Data were segmented using a freely available online tool called Relayer ( https://www.relayer.online/ ), based on a cross-platform Deep Learning segmentation architecture specifically adapted for retinal OCT images. The segmentations were read into MATLAB (The MathWorks, Natick, MA, USA) and analyzed.

Results: There was an excellent agreement between the ETDRS measurements obtained from the two algorithms. Upon visual inspection, the segmentation based on Deep Learning obtained with Relayer appeared more accurate except in one case of apparent good quality image showing interrupted segmentations in some of the B-scans.

Conclusion: A freely available online Deep Learning segmentation tool showed good and promising performance in healthy retinas before and after cataract surgery, proving robust to optical degradation of the image from media opacities.

Keywords: Cataract optical degradation; Deep Learning; OCT; Optical coherence tomography; Segmentation algorithm.