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Automated segmentation and description of the internal morphology of human permanent teeth by means of micro-CT

High-resolution micro-computed tomography is a powerful tool to analyze and visualize the internal morphology of human permanent teeth. It is increasingly used for investigation of epidemiological questions to provide the dentist with the necessary information required for successful endodontic trea...

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Autores principales: Haberthür, David, Hlushchuk, Ruslan, Wolf, Thomas Gerhard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8040229/
https://www.ncbi.nlm.nih.gov/pubmed/33845806
http://dx.doi.org/10.1186/s12903-021-01551-x
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author Haberthür, David
Hlushchuk, Ruslan
Wolf, Thomas Gerhard
author_facet Haberthür, David
Hlushchuk, Ruslan
Wolf, Thomas Gerhard
author_sort Haberthür, David
collection PubMed
description High-resolution micro-computed tomography is a powerful tool to analyze and visualize the internal morphology of human permanent teeth. It is increasingly used for investigation of epidemiological questions to provide the dentist with the necessary information required for successful endodontic treatment. The aim of the present paper was to propose an image processing method to automate parts of the work needed to fully describe the internal morphology of human permanent teeth. One hundred and four human teeth were scanned on a high-resolution micro-CT scanner using an automatic specimen changer. Python code in a Jupyter notebook was used to verify and process the scans, prepare the datasets for description of the internal morphology and to measure the apical region of the tooth. The presented method offers an easy, non-destructive, rapid and efficient approach to scan, check and preview tomographic datasets of a large number of teeth. It is a helpful tool for the detailed description and characterization of the internal morphology of human permanent teeth using automated segmentation by means of micro-CT with full reproducibility and high standardization. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12903-021-01551-x.
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spelling pubmed-80402292021-04-12 Automated segmentation and description of the internal morphology of human permanent teeth by means of micro-CT Haberthür, David Hlushchuk, Ruslan Wolf, Thomas Gerhard BMC Oral Health Research High-resolution micro-computed tomography is a powerful tool to analyze and visualize the internal morphology of human permanent teeth. It is increasingly used for investigation of epidemiological questions to provide the dentist with the necessary information required for successful endodontic treatment. The aim of the present paper was to propose an image processing method to automate parts of the work needed to fully describe the internal morphology of human permanent teeth. One hundred and four human teeth were scanned on a high-resolution micro-CT scanner using an automatic specimen changer. Python code in a Jupyter notebook was used to verify and process the scans, prepare the datasets for description of the internal morphology and to measure the apical region of the tooth. The presented method offers an easy, non-destructive, rapid and efficient approach to scan, check and preview tomographic datasets of a large number of teeth. It is a helpful tool for the detailed description and characterization of the internal morphology of human permanent teeth using automated segmentation by means of micro-CT with full reproducibility and high standardization. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12903-021-01551-x. BioMed Central 2021-04-12 /pmc/articles/PMC8040229/ /pubmed/33845806 http://dx.doi.org/10.1186/s12903-021-01551-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Haberthür, David
Hlushchuk, Ruslan
Wolf, Thomas Gerhard
Automated segmentation and description of the internal morphology of human permanent teeth by means of micro-CT
title Automated segmentation and description of the internal morphology of human permanent teeth by means of micro-CT
title_full Automated segmentation and description of the internal morphology of human permanent teeth by means of micro-CT
title_fullStr Automated segmentation and description of the internal morphology of human permanent teeth by means of micro-CT
title_full_unstemmed Automated segmentation and description of the internal morphology of human permanent teeth by means of micro-CT
title_short Automated segmentation and description of the internal morphology of human permanent teeth by means of micro-CT
title_sort automated segmentation and description of the internal morphology of human permanent teeth by means of micro-ct
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8040229/
https://www.ncbi.nlm.nih.gov/pubmed/33845806
http://dx.doi.org/10.1186/s12903-021-01551-x
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