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

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).

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 train the classifier. Results were generated as a probability map. Thresholding was performed to remove pixels that were classified with low confidence. Volume rendering was performed with TomViz.

Forty-seven eyes of 34 healthy subjects were imaged with SS-OCT. Thirty-four cube scans from 25 subjects were of sufficient quality for volume rendering. Clinically relevant vitreous features including the premacular bursa, area of Martegiani, and prevascular vitreous fissures and cisterns, as well as varying degrees of vitreous degeneration were visualized in 3D.

A machine-learning model for 3D vitreous reconstruction of SS-OCT cube scans was developed. The resultant high-resolution 3D movies illustrated vitreous anatomy in a manner like triamcinolone-assisted vitrectomy or postmortem dye injection.

This machine learning model now allows for comprehensive examination of the vitreous structure beyond the vitreoretinal interface in 3D with potential applications for common disease states such as the vitreomacular traction and Macular Hole spectrum of diseases or proliferative diabetic retinopathy.