A Pixel-Based Machine-Learning Model For Three-Dimensional Reconstruction of Vitreous Anatomy.

ABSTRACT:

Purpose

To develop a machine-learning image processing model for three-dimensional (3D) reconstruction of vitreous anatomy visualized with swept-source optical coherence tomography (SS-OCT).

Methods

Healthy subjects were imaged with SS-OCT. Scans of sufficient quality were transferred into the Fiji is just ImageJ image processing toolkit, and proportions of the resulting stacks were adjusted to form cubic voxels. Image-averaging and Trainable Weka Segmentation using Sobel and variance edge detection and directional membrane projections filters were used to enhance and interpret the signals from vitreous gel, liquid spaces within the vitreous, and interfaces between the former. Two classes were defined: "Septa" and "Other." Pixels were selected and added to each class to trai

SUBMITTER: Thi A 

PROVIDER: S-EPMC9279921 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature