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

Cited 4 time in Web of Science Cited 4 time in Scopus
Authors

Kim, Sul-Hee; Kim, Jin; Yang, Su; Oh, Sung-Hye; Lee, Seung-Pyo; Yang, Hoon Joo; Kim, Tae-Il; Yi, Won Jin

Issue Date
2022-10
Publisher
The Optical Society
Citation
Biomedical Optics Express, Vol.13 No.10, pp.5468-5482
Abstract
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.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.
ISSN
2156-7085
URI
https://hdl.handle.net/10371/190175
DOI
https://doi.org/10.1364/BOE.468212
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