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.
本文旨在简化光学相干断层扫描（OCT）在口腔亚表面形态检查中的应用，并降低其作为活检引导设备的应用门槛。本研究的目标是开发自动化软件工具，以简化分析OCT采集的大规模数据。通过宽视场内窥镜OCT系统在临床环境中获取影像及对应的组织病理学数据。研究收集了来自60名患者（34名男性，26名女性）的标注数据集（共294张图像），用于训练四个独立的神经网络。采用卷积神经网络和改进的U-Net模型构建深度学习流程，分别实现成像视场检测（网络1）、伪影识别（网络2）、组织表面定位（网络3）以及上皮-基质边界存在性及位置判定（网络4）。影像检测网络与伪影识别网络的曲线下面积分别为1.00和0.94，表面分割网络与上皮-基质边界分割网络的戴斯相似系数分别为0.98和0.83。深度学习技术能够准确定位口腔黏膜OCT图像中上皮表面及上皮-基质边界的位置与形态变异，其分割结果可整合为直观的正面投影图谱，从而更清晰地呈现组织形态变化。
肿瘤(癌症)患者之家