Hill, C.; Malone, J.; Liu, K.; Ng, S.P.-Y.; MacAulay, C.; Poh, C.; Lane, P. Three-Dimension Epithelial Segmentation in Optical Coherence Tomography of the Oral Cavity Using Deep Learning. Cancers2024, 16, 2144.
Hill, C.; Malone, J.; Liu, K.; Ng, S.P.-Y.; MacAulay, C.; Poh, C.; Lane, P. Three-Dimension Epithelial Segmentation in Optical Coherence Tomography of the Oral Cavity Using Deep Learning. Cancers 2024, 16, 2144.
Hill, C.; Malone, J.; Liu, K.; Ng, S.P.-Y.; MacAulay, C.; Poh, C.; Lane, P. Three-Dimension Epithelial Segmentation in Optical Coherence Tomography of the Oral Cavity Using Deep Learning. Cancers2024, 16, 2144.
Hill, C.; Malone, J.; Liu, K.; Ng, S.P.-Y.; MacAulay, C.; Poh, C.; Lane, P. Three-Dimension Epithelial Segmentation in Optical Coherence Tomography of the Oral Cavity Using Deep Learning. Cancers 2024, 16, 2144.
Abstract
Purpose: To simplify the application of optical coherence tomography (OCT) for examination of subsurface morphology in the oral cavity and reduce barriers towards adoption of OCT as a biopsy guidance device. The aim of this work was to develop automated software tools for simplified analysis of the large volume of data collected during OCT. Methods: 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, 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 basement membrane (network 4). Results: 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 basement membrane segmentation networks was 0.98 and 0.83 respectively. Conclusion: Deep Learning (DL) techniques can identify the location and variations in the epithelial surface and basement membrane in OCT images of the oral mucosa. Segmentation results can be synthesized into accessible en face maps to allow easier visualization of changes.
Keywords
optical coherence tomography; oral cancer; deep learning; segmentation; cancer detection and diagnosis system
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
