3D Deep Learning-based Boundary Regression of an Age-related Retinal Biomarker in High Resolution OCT

Abstract

Vision is essential for quality of life, but is threatened by visionimpairing diseases like age-related macular degeneration (AMD). A recently proposed biomarker potentially to distinguish normal aging from AMD is the gap visualized between the retinal pigment epithelium (RPE) and the Bruch’s membrane. Due to lack of automated processing, to date, this gap was only described sparsely in histologic data or on optical coherence tomography (OCT) B-scans. By segmenting the posterior RPE boundary automatically for the first time, we enable fully-automatic quantification of the thickness of this gap in vivo across whole volumetric OCT images. Our novel processing pipeline leverages advancements in motion correction, volumetric image merging, and high resolution OCT. A novel 3D boundary regression network named depth map regression network (DMR-Net) estimates the gap thickness in the volume. As 3D networks require full-volume ground truth boundary labels, which are labor-intensive, we developed a novel semi-automatic labeling approach to refine existing labels based on the visibility of the gap with minimal user input. We demonstrate thickness maps across a wide age range of healthy participants (23 – 79 years). The median absolute error in the test set is 0.161 μm, which is well below the axial pixel spacing (0.89 μm). For the first time, our results allow spatially resolved analysis to investigate pathologic deviations in normal aging and AMD.