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Automatic mandibular canal detection using a deep convolutional neural network

The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being wid...

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Autores principales: Kwak, Gloria Hyunjung, Kwak, Eun-Jung, Song, Jae Min, Park, Hae Ryoun, Jung, Yun-Hoa, Cho, Bong-Hae, Hui, Pan, Hwang, Jae Joon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7109125/
https://www.ncbi.nlm.nih.gov/pubmed/32235882
http://dx.doi.org/10.1038/s41598-020-62586-8
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author Kwak, Gloria Hyunjung
Kwak, Eun-Jung
Song, Jae Min
Park, Hae Ryoun
Jung, Yun-Hoa
Cho, Bong-Hae
Hui, Pan
Hwang, Jae Joon
author_facet Kwak, Gloria Hyunjung
Kwak, Eun-Jung
Song, Jae Min
Park, Hae Ryoun
Jung, Yun-Hoa
Cho, Bong-Hae
Hui, Pan
Hwang, Jae Joon
author_sort Kwak, Gloria Hyunjung
collection PubMed
description The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being widely researched. Particularly in dentistry, for various reasons, automated mandibular canal detection has become highly desirable. The positioning of the inferior alveolar nerve (IAN), which is one of the major structures in the mandible, is crucial to prevent nerve injury during surgical procedures. However, automatic segmentation using Cone beam computed tomography (CBCT) poses certain difficulties, such as the complex appearance of the human skull, limited number of datasets, unclear edges, and noisy images. Using work-in-progress automation software, experiments were conducted with models based on 2D SegNet, 2D and 3D U-Nets as preliminary research for a dental segmentation automation tool. The 2D U-Net with adjacent images demonstrates higher global accuracy of 0.82 than naïve U-Net variants. The 2D SegNet showed the second highest global accuracy of 0.96, and the 3D U-Net showed the best global accuracy of 0.99. The automated canal detection system through deep learning will contribute significantly to efficient treatment planning and to reducing patients’ discomfort by a dentist. This study will be a preliminary report and an opportunity to explore the application of deep learning to other dental fields.
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spelling pubmed-71091252020-04-06 Automatic mandibular canal detection using a deep convolutional neural network Kwak, Gloria Hyunjung Kwak, Eun-Jung Song, Jae Min Park, Hae Ryoun Jung, Yun-Hoa Cho, Bong-Hae Hui, Pan Hwang, Jae Joon Sci Rep Article The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being widely researched. Particularly in dentistry, for various reasons, automated mandibular canal detection has become highly desirable. The positioning of the inferior alveolar nerve (IAN), which is one of the major structures in the mandible, is crucial to prevent nerve injury during surgical procedures. However, automatic segmentation using Cone beam computed tomography (CBCT) poses certain difficulties, such as the complex appearance of the human skull, limited number of datasets, unclear edges, and noisy images. Using work-in-progress automation software, experiments were conducted with models based on 2D SegNet, 2D and 3D U-Nets as preliminary research for a dental segmentation automation tool. The 2D U-Net with adjacent images demonstrates higher global accuracy of 0.82 than naïve U-Net variants. The 2D SegNet showed the second highest global accuracy of 0.96, and the 3D U-Net showed the best global accuracy of 0.99. The automated canal detection system through deep learning will contribute significantly to efficient treatment planning and to reducing patients’ discomfort by a dentist. This study will be a preliminary report and an opportunity to explore the application of deep learning to other dental fields. Nature Publishing Group UK 2020-03-31 /pmc/articles/PMC7109125/ /pubmed/32235882 http://dx.doi.org/10.1038/s41598-020-62586-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Kwak, Gloria Hyunjung
Kwak, Eun-Jung
Song, Jae Min
Park, Hae Ryoun
Jung, Yun-Hoa
Cho, Bong-Hae
Hui, Pan
Hwang, Jae Joon
Automatic mandibular canal detection using a deep convolutional neural network
title Automatic mandibular canal detection using a deep convolutional neural network
title_full Automatic mandibular canal detection using a deep convolutional neural network
title_fullStr Automatic mandibular canal detection using a deep convolutional neural network
title_full_unstemmed Automatic mandibular canal detection using a deep convolutional neural network
title_short Automatic mandibular canal detection using a deep convolutional neural network
title_sort automatic mandibular canal detection using a deep convolutional neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7109125/
https://www.ncbi.nlm.nih.gov/pubmed/32235882
http://dx.doi.org/10.1038/s41598-020-62586-8
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