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.
OCT is a promising imaging modality for the field of dermatology, but lack of research is hinders its use in everyday practice. Our study provides qualitative and quantitative analysis of healthy skin from various anatomic locations. Qualitative analysis showed identification of key structures including epidermis, dermis, and dermal epidermal junction (DEJ), as well as accessory structures. Quantitative analysis produced a characteristic absorption spectra of healthy skin, with a peaked intensity at stratum corneum, second peak at DEJ, and variations due to accessory structures. This analysis is valuable for OCTs use as a diagnostic aid in determining healthy vs pathologic skin.
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