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Open AccessArticle

OCT-Based Periodontal Inspection Framework

1
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan
2
Department of Dentistry, National Yang-Ming University, Taipei 112, Taiwan
3
Department of Stomatology, Taipei Veterans General Hospital, Taipei 112, Taiwan
4
Department of Dentistry, Taipei City Hospital, Taipei 103, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(24), 5496; https://doi.org/10.3390/s19245496
Received: 23 October 2019 / Revised: 7 December 2019 / Accepted: 9 December 2019 / Published: 12 December 2019
Periodontal diagnosis requires discovery of the relations among teeth, gingiva (i.e., gums), and alveolar bones, but alveolar bones are inside gingiva and not visible for inspection. Traditional probe examination causes pain, and X-ray based examination is not suited for frequent inspection. This work develops an automatic non-invasive periodontal inspection framework based on gum penetrative Optical Coherence Tomography (OCT), which can be frequently applied without high radiation. We sum up interference responses of all penetration depths for all shooting directions respectively to form the shooting amplitude projection. Because the reaching interference strength decays exponentially with tissues’ penetration depth, this projection mainly reveals the responses of the top most gingiva or teeth. Since gingiva and teeth have different air-tissue responses, the gumline, revealing itself as an obvious boundary between teeth and gingiva, is the basis line for periodontal inspection. Our system can also automatically identify regions of gingiva, teeth, and alveolar bones from slices of the cross-sectional volume. Although deep networks can successfully and possibly segment noisy maps, reducing the number of manually labeled maps for training is critical for our framework. In order to enhance the effectiveness and efficiency of training and classification, we adjust Snake segmentation to consider neighboring slices in order to locate those regions possibly containing gingiva-teeth and gingiva–alveolar boundaries. Additionally, we also adapt a truncated direct logarithm based on the Snake-segmented region for intensity quantization to emphasize these boundaries for easier identification. Later, the alveolar-gingiva boundary point directly under the gumline is the desired alveolar sample, and we can measure the distance between the gumline and alveolar line for visualization and direct periodontal inspection. At the end, we experimentally verify our choice in intensity quantization and boundary identification against several other algorithms while applying the framework to locate gumline and alveolar line in vivo data successfully. View Full-Text
Keywords: OCT based periodontal inspector; periodontal examination; deep network identifier OCT based periodontal inspector; periodontal examination; deep network identifier
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Lai, Y.-C.; Chiu, C.-H.; Cai, Z.-Q.; Lin, J.-Y.; Yao, C.-Y.; Lyu, D.-Y.; Lee, S.-Y.; Chen, K.-W.; Chen, I.-Y. OCT-Based Periodontal Inspection Framework. Sensors 2019, 19, 5496.

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