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Semantic Segmentation of Maxillary Teeth and Palatal Rugae in Two-Dimensional Images

The superimposition of sequential radiographs of the head is commonly used to determine the amount and direction of orthodontic tooth movement. A harmless method includes the timely unlimited superimposition on the relatively stable palatal rugae, but the method is performed manually and, if automat...

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Autores principales: El Bsat, Abdul Rehman, Shammas, Elie, Asmar, Daniel, Sakr, George E., Zeno, Kinan G., Macari, Anthony T., Ghafari, Joseph G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498073/
https://www.ncbi.nlm.nih.gov/pubmed/36140577
http://dx.doi.org/10.3390/diagnostics12092176
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author El Bsat, Abdul Rehman
Shammas, Elie
Asmar, Daniel
Sakr, George E.
Zeno, Kinan G.
Macari, Anthony T.
Ghafari, Joseph G.
author_facet El Bsat, Abdul Rehman
Shammas, Elie
Asmar, Daniel
Sakr, George E.
Zeno, Kinan G.
Macari, Anthony T.
Ghafari, Joseph G.
author_sort El Bsat, Abdul Rehman
collection PubMed
description The superimposition of sequential radiographs of the head is commonly used to determine the amount and direction of orthodontic tooth movement. A harmless method includes the timely unlimited superimposition on the relatively stable palatal rugae, but the method is performed manually and, if automated, relies on the best fit of surfaces, not only rugal structures. In the first step, motion estimation requires segmenting and detecting the location of teeth and rugae at any time during the orthodontic intervention. Aim: to develop a process of tooth segmentation that eliminates all manual steps to achieve an autonomous system of assessment of the dentition. Methods: A dataset of 797 occlusal views from photographs of teeth was created. The photographs were manually semantically segmented and labeled. Machine learning methods were applied to identify a robust deep network architecture able to semantically segment teeth in unseen photographs. Using well-defined metrics such as accuracy, precision, and the average mean intersection over union (mIoU), four network architectures were tested: MobileUnet, AdapNet, DenseNet, and SegNet. The robustness of the trained network was additionally tested on a set of 47 image pairs of patients before and after orthodontic treatment. Results: SegNet was the most accurate network, producing 95.19% accuracy and an average mIoU value of 86.66% for the main sample and 86.2% for pre- and post-treatment images. Conclusions: Four architectural tests were developed for automated individual teeth segmentation and detection in two-dimensional photos that required no post-processing. Accuracy and robustness were best achieved with SegNet. Further research should focus on clinical applications and 3D system development.
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spelling pubmed-94980732022-09-23 Semantic Segmentation of Maxillary Teeth and Palatal Rugae in Two-Dimensional Images El Bsat, Abdul Rehman Shammas, Elie Asmar, Daniel Sakr, George E. Zeno, Kinan G. Macari, Anthony T. Ghafari, Joseph G. Diagnostics (Basel) Article The superimposition of sequential radiographs of the head is commonly used to determine the amount and direction of orthodontic tooth movement. A harmless method includes the timely unlimited superimposition on the relatively stable palatal rugae, but the method is performed manually and, if automated, relies on the best fit of surfaces, not only rugal structures. In the first step, motion estimation requires segmenting and detecting the location of teeth and rugae at any time during the orthodontic intervention. Aim: to develop a process of tooth segmentation that eliminates all manual steps to achieve an autonomous system of assessment of the dentition. Methods: A dataset of 797 occlusal views from photographs of teeth was created. The photographs were manually semantically segmented and labeled. Machine learning methods were applied to identify a robust deep network architecture able to semantically segment teeth in unseen photographs. Using well-defined metrics such as accuracy, precision, and the average mean intersection over union (mIoU), four network architectures were tested: MobileUnet, AdapNet, DenseNet, and SegNet. The robustness of the trained network was additionally tested on a set of 47 image pairs of patients before and after orthodontic treatment. Results: SegNet was the most accurate network, producing 95.19% accuracy and an average mIoU value of 86.66% for the main sample and 86.2% for pre- and post-treatment images. Conclusions: Four architectural tests were developed for automated individual teeth segmentation and detection in two-dimensional photos that required no post-processing. Accuracy and robustness were best achieved with SegNet. Further research should focus on clinical applications and 3D system development. MDPI 2022-09-08 /pmc/articles/PMC9498073/ /pubmed/36140577 http://dx.doi.org/10.3390/diagnostics12092176 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
El Bsat, Abdul Rehman
Shammas, Elie
Asmar, Daniel
Sakr, George E.
Zeno, Kinan G.
Macari, Anthony T.
Ghafari, Joseph G.
Semantic Segmentation of Maxillary Teeth and Palatal Rugae in Two-Dimensional Images
title Semantic Segmentation of Maxillary Teeth and Palatal Rugae in Two-Dimensional Images
title_full Semantic Segmentation of Maxillary Teeth and Palatal Rugae in Two-Dimensional Images
title_fullStr Semantic Segmentation of Maxillary Teeth and Palatal Rugae in Two-Dimensional Images
title_full_unstemmed Semantic Segmentation of Maxillary Teeth and Palatal Rugae in Two-Dimensional Images
title_short Semantic Segmentation of Maxillary Teeth and Palatal Rugae in Two-Dimensional Images
title_sort semantic segmentation of maxillary teeth and palatal rugae in two-dimensional images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498073/
https://www.ncbi.nlm.nih.gov/pubmed/36140577
http://dx.doi.org/10.3390/diagnostics12092176
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