Epidermal Thickness Measurement on Skin OCT using Time-efficient Deep Learning with Graph Search

Optical coherence tomography (OCT) images enable the visualization of cell layers, and accurate layer thickness is crucial for disease diagnosis and treatment tracking. To measure layer thickness, delineating the layer boundaries is the first step. In this paper, we proposed a time-efficient layer segmentation method developed on central unit processors (CPUs). This method consists of convolutional neural networks (CNN) and graph search (CNN-GS). CNN-GS aims to automatically segment two defined boundaries to calculate the epidermal thickness. We applied our method to 110 skin OCT images from various body locations, taken from 13 healthy individuals aged between 20 and 60 years, to evaluate the performance and versatility of our method. Our method demonstrated an overall 94.68% accuracy on patch-wise classification and an 85.81% accuracy on segmentation position accuracy as compared to manual segmentation, allowing 94.87% accuracy on epidermal thickness. In addition, our method performed a near real-time image analysis, costing less than 1 second per skin OCT image to delineate the layer boundaries.