Novel segmentation algorithm for high-throughput analysis of spectral domain-optical coherence tomography imaging of teleost retinas

Spectral domain-optical coherence tomography (SD-OCT) has become an essential tool for assessing ocular tissues in live subjects and conducting research on ocular development, health, and disease. The processing of SD-OCT images, particularly those from non-mammalian species, is a labor-intensive manual process due to a lack of automated analytical programs. This paper describes the development and implementation of a novel computer algorithm for the quantitative analysis of SD-OCT images of live teleost eyes. Automated segmentation processing of SD-OCT images of retinal layers was developed using a novel algorithm based on thresholding. The algorithm measures retinal thickness characteristics in a large volume of imaging data of teleost ocular structures in a short time, providing increased accuracy and repeatability of SD-OCT image analysis over manual measurements. The algorithm also generates hundreds of retinal thickness measurements per image for a large number of images for a given dataset. Meanwhile, heat mapping software that plots SD-OCT image measurements as a color gradient was also created. This software directly converts the measurements of each processed image to represent changes in thickness across the whole retinal scan. It also enables 2D and 3D visualization of retinal thickness across the scan, facilitating specimen comparison and localization of areas of interest. The study findings showed that the novel algorithm is more accurate, reliable, and repeatable than manual SD-OCT analysis. The adaptability of the algorithm makes it potentially suitable for analyzing SD-OCT scans of other non-mammalian species.