Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method

Abstract 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 fr...

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Autores principales:	Young Hyun Kim, Jin Young Shin, Ari Lee, Seungtae Park, Sang-Sun Han, Hyung Ju Hwang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1a9b71336bdc46efb27935a6b6e72669
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spelling	oai:doaj.org-article:1a9b71336bdc46efb27935a6b6e726692021-12-02T16:17:17ZAutomated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method10.1038/s41598-021-94362-72045-2322https://doaj.org/article/1a9b71336bdc46efb27935a6b6e726692021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94362-7https://doaj.org/toc/2045-2322Abstract 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.Young Hyun KimJin Young ShinAri LeeSeungtae ParkSang-Sun HanHyung Ju HwangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Young Hyun Kim Jin Young Shin Ari Lee Seungtae Park Sang-Sun Han Hyung Ju Hwang Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method
description	Abstract 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	article
author	Young Hyun Kim Jin Young Shin Ari Lee Seungtae Park Sang-Sun Han Hyung Ju Hwang
author_facet	Young Hyun Kim Jin Young Shin Ari Lee Seungtae Park Sang-Sun Han Hyung Ju Hwang
author_sort	Young Hyun Kim
title	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_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_sort	automated cortical thickness measurement of the mandibular condyle head on cbct images using a deep learning method
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1a9b71336bdc46efb27935a6b6e72669
work_keys_str_mv	AT younghyunkim automatedcorticalthicknessmeasurementofthemandibularcondyleheadoncbctimagesusingadeeplearningmethod AT jinyoungshin automatedcorticalthicknessmeasurementofthemandibularcondyleheadoncbctimagesusingadeeplearningmethod AT arilee automatedcorticalthicknessmeasurementofthemandibularcondyleheadoncbctimagesusingadeeplearningmethod AT seungtaepark automatedcorticalthicknessmeasurementofthemandibularcondyleheadoncbctimagesusingadeeplearningmethod AT sangsunhan automatedcorticalthicknessmeasurementofthemandibularcondyleheadoncbctimagesusingadeeplearningmethod AT hyungjuhwang automatedcorticalthicknessmeasurementofthemandibularcondyleheadoncbctimagesusingadeeplearningmethod
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Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method

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