Automatic and quantitative measurement of alveolar bone level in OCT images using deep learning

We propose a method to automatically segment the periodontal structures of the tooth enamel and the alveolar bone using convolutional neural network (CNN) and to measure quantitatively and automatically the alveolar bone level (ABL) by detecting the cemento-enamel junction and the alveolar bone crest in optical coherence tomography (OCT) images. The tooth enamel and the alveolar bone regions were automatically segmented using U-Net, Dense-UNet, and U2-Net, and the ABL was quantitatively measured as the distance between the cemento-enamel junction and the alveolar bone crest using image processing. The mean distance difference (MDD) measured by our suggested method ranged from 0.19 to 0.22 mm for the alveolar bone crest (ABC) and from 0.18 to 0.32 mm for the cemento-enamel junction (CEJ). All CNN models showed the mean absolute error (MAE) of less than 0.25 mm in the x and y coordinates and greater than 90% successful detection rate (SDR) at 0.5 mm for both the ABC and the CEJ. The CNN models showed high segmentation accuracies in the tooth enamel and the alveolar bone regions, and the ABL measurements at the incisors by detected results from CNN predictions demonstrated high correlation and reliability with the ground truth in OCT images. (c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

This study proposes a method for automatic segmentation of tooth enamel and alveolar bone using convolutional neural network (CNN) and quantitatively and automatically measuring the alveolar bone level (ABL) in optical coherence tomography (OCT) images. The experimental results demonstrate high segmentation accuracy in the tooth enamel and alveolar bone regions, and the measured results show a high correlation and reliability with the ground truth in OCT images.