Three-Dimension Epithelial Segmentation in Optical Coherence Tomography of the Oral Cavity Using Deep Learning.

Simple Summary: The diagnosis of oral cancer can require multiple biopsies to increase the likelihood of sampling the most pathologic site within a lesion. Optical coherence tomography (OCT) enables the examination of subsurface morphology and has shown potential in biopsy guidance. OCT captures changes in tissue stratification related to depth, topology, and presence of the epithelial-stromal boundary, which are structural biomarkers for pre-invasive and invasive oral cancer. This study presents a neural network pipeline to simplify OCT interpretation by providing information about epithelial depth and stratification through simple en face maps. U-net models were employed to segment the boundaries of the epithelial layer, and supporting convolutional neural networks were used for identification of the imaging field and artifacts. Non-cancerous, precancerous, and cancerous pathologies across the oral cavity were evaluated. The predictions demonstrate as-good-as or better agreement than inter-rater agreement, suggesting strong predictive power. This paper aims to simplify the application of optical coherence tomography (OCT) for the examination of subsurface morphology in the oral cavity and reduce barriers towards the adoption of OCT as a biopsy guidance device. The aim of this work was to develop automated software tools for the simplified analysis of the large volume of data collected during OCT. Imaging and corresponding histopathology were acquired in-clinic using a wide-field endoscopic OCT system. An annotated dataset (n = 294 images) from 60 patients (34 male and 26 female) was assembled to train four unique neural networks. A deep learning pipeline was built using convolutional and modified u-net models to detect the imaging field of view (network 1), detect artifacts (network 2), identify the tissue surface (network 3), and identify the presence and location of the epithelial–stromal boundary (network 4). The area under the curve of the image and artifact detection networks was 1.00 and 0.94, respectively. The Dice similarity score for the surface and epithelial–stromal boundary segmentation networks was 0.98 and 0.83, respectively. Deep learning (DL) techniques can identify the location and variations in the epithelial surface and epithelial–stromal boundary in OCT images of the oral mucosa. Segmentation results can be synthesized into accessible en face maps to allow easier visualization of changes. [ABSTRACT FROM AUTHOR]