3D Graph-Based Automated Segmentation of Corneal Layers in Anterior-Segment Optical Coherence Tomography Images of Mice

Anterior segment optical coherence tomography (AS-OCT) is a non-invasive imaging modality that allows for the quantitative assessment of corneal thicknesses. Automated approaches for these measurements are not readily available and therefore measurements are often obtained manually. While graph-based approaches that enable the optimal simultaneous segmentation of multiple 3D surfaces have been proposed and applied to 3D optical coherence tomography volumes of the back of the eye, such approaches have not been applied for the segmentation of the corneal surfaces. In this work we propose adapting this graph-based method for the automated 3D segmentation of three corneal surfaces in AS-OCT images and to measure total corneal thickness. The approach is evaluated based on 34 AS-OCT volumes obtained from both eyes of 17 mice with varying corneal thicknesses. The segmentation accuracy was assessed using unsigned border positioning errors and was found to be 1.82 0.81 pm. We also assessed an average relative error in total layer thickness measurements which was found to be 2.27%.