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Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method
This study proposes a deep learning model for cortical bone segmentation in the mandibular condyle head using cone-beam computed tomography (CBCT) and an automated method for measuring cortical thickness with a color display based on the segmentation results. In total, 12,800 CBCT images from 25 nor...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8295413/ https://www.ncbi.nlm.nih.gov/pubmed/34290333 http://dx.doi.org/10.1038/s41598-021-94362-7 |
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author | Kim, Young Hyun Shin, Jin Young Lee, Ari Park, Seungtae Han, Sang-Sun Hwang, Hyung Ju |
author_facet | Kim, Young Hyun Shin, Jin Young Lee, Ari Park, Seungtae Han, Sang-Sun Hwang, Hyung Ju |
author_sort | Kim, Young Hyun |
collection | PubMed |
description | This study proposes a deep learning model for cortical bone segmentation in the mandibular condyle head using cone-beam computed tomography (CBCT) and an automated method for measuring cortical thickness with a color display based on the segmentation results. In total, 12,800 CBCT images from 25 normal subjects, manually labeled by an oral radiologist, served as the gold-standard. The segmentation model combined a modified U-Net and a convolutional neural network for target region classification. Model performance was evaluated using intersection over union (IoU) and the Hausdorff distance in comparison with the gold standard. The second automated model measured the cortical thickness based on a three-dimensional (3D) model rendered from the segmentation results and presented a color visualization of the measurements. The IoU and Hausdorff distance showed high accuracy (0.870 and 0.928 for marrow bone and 0.734 and 1.247 for cortical bone, respectively). A visual comparison of the 3D color maps showed a similar trend to the gold standard. This algorithm for automatic segmentation of the mandibular condyle head and visualization of the measured cortical thickness as a 3D-rendered model with a color map may contribute to the automated quantification of bone thickness changes of the temporomandibular joint complex on CBCT. |
format | Online Article Text |
id | pubmed-8295413 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-82954132021-07-23 Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method Kim, Young Hyun Shin, Jin Young Lee, Ari Park, Seungtae Han, Sang-Sun Hwang, Hyung Ju Sci Rep Article This study proposes a deep learning model for cortical bone segmentation in the mandibular condyle head using cone-beam computed tomography (CBCT) and an automated method for measuring cortical thickness with a color display based on the segmentation results. In total, 12,800 CBCT images from 25 normal subjects, manually labeled by an oral radiologist, served as the gold-standard. The segmentation model combined a modified U-Net and a convolutional neural network for target region classification. Model performance was evaluated using intersection over union (IoU) and the Hausdorff distance in comparison with the gold standard. The second automated model measured the cortical thickness based on a three-dimensional (3D) model rendered from the segmentation results and presented a color visualization of the measurements. The IoU and Hausdorff distance showed high accuracy (0.870 and 0.928 for marrow bone and 0.734 and 1.247 for cortical bone, respectively). A visual comparison of the 3D color maps showed a similar trend to the gold standard. This algorithm for automatic segmentation of the mandibular condyle head and visualization of the measured cortical thickness as a 3D-rendered model with a color map may contribute to the automated quantification of bone thickness changes of the temporomandibular joint complex on CBCT. Nature Publishing Group UK 2021-07-21 /pmc/articles/PMC8295413/ /pubmed/34290333 http://dx.doi.org/10.1038/s41598-021-94362-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kim, Young Hyun Shin, Jin Young Lee, Ari Park, Seungtae Han, Sang-Sun Hwang, Hyung Ju Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method |
title | Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method |
title_full | Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method |
title_fullStr | Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method |
title_full_unstemmed | Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method |
title_short | Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method |
title_sort | automated cortical thickness measurement of the mandibular condyle head on cbct images using a deep learning method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8295413/ https://www.ncbi.nlm.nih.gov/pubmed/34290333 http://dx.doi.org/10.1038/s41598-021-94362-7 |
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