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Deep learning-based dental implant recognition using synthetic X-ray images

ABSTRACT: A novel algorithm for generating artificial training samples from triangulated three-dimensional (3D) surface models within the context of dental implant recognition is proposed. The proposed algorithm is based on the calculation of two-dimensional (2D) projections (from a number of differ...

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Autores principales: Kohlakala, Aviwe, Coetzer, Johannes, Bertels, Jeroen, Vandermeulen, Dirk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385426/
https://www.ncbi.nlm.nih.gov/pubmed/35978215
http://dx.doi.org/10.1007/s11517-022-02642-9
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author Kohlakala, Aviwe
Coetzer, Johannes
Bertels, Jeroen
Vandermeulen, Dirk
author_facet Kohlakala, Aviwe
Coetzer, Johannes
Bertels, Jeroen
Vandermeulen, Dirk
author_sort Kohlakala, Aviwe
collection PubMed
description ABSTRACT: A novel algorithm for generating artificial training samples from triangulated three-dimensional (3D) surface models within the context of dental implant recognition is proposed. The proposed algorithm is based on the calculation of two-dimensional (2D) projections (from a number of different angles) of 3D volumetric representations of computer-aided design (CAD) surface models. A fully convolutional network (FCN) is subsequently trained on the artificially generated X-ray images for the purpose of automatically identifying the connection type associated with a specific dental implant in an actual X-ray image. Semi-automated and fully automated systems are proposed for segmenting questioned dental implants from the background in actual X-ray images. Within the context of the semi-automated system, suitable regions of interest (ROIs), which contain the dental implants, are manually specified. However, as part of the fully automated system, suitable ROIs are automatically detected. It is demonstrated that a segmentation/detection accuracy of 94.0% and a classification/recognition accuracy of 71.7% are attainable within the context of the proposed fully automated system. Since the proposed systems utilise an ensemble of techniques that has not been employed for the purpose of dental implant classification/recognition on any previous occasion, the above-mentioned results are very encouraging. GRAPHICAL ABSTRACT: [Image: see text]
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spelling pubmed-93854262022-08-18 Deep learning-based dental implant recognition using synthetic X-ray images Kohlakala, Aviwe Coetzer, Johannes Bertels, Jeroen Vandermeulen, Dirk Med Biol Eng Comput Original Article ABSTRACT: A novel algorithm for generating artificial training samples from triangulated three-dimensional (3D) surface models within the context of dental implant recognition is proposed. The proposed algorithm is based on the calculation of two-dimensional (2D) projections (from a number of different angles) of 3D volumetric representations of computer-aided design (CAD) surface models. A fully convolutional network (FCN) is subsequently trained on the artificially generated X-ray images for the purpose of automatically identifying the connection type associated with a specific dental implant in an actual X-ray image. Semi-automated and fully automated systems are proposed for segmenting questioned dental implants from the background in actual X-ray images. Within the context of the semi-automated system, suitable regions of interest (ROIs), which contain the dental implants, are manually specified. However, as part of the fully automated system, suitable ROIs are automatically detected. It is demonstrated that a segmentation/detection accuracy of 94.0% and a classification/recognition accuracy of 71.7% are attainable within the context of the proposed fully automated system. Since the proposed systems utilise an ensemble of techniques that has not been employed for the purpose of dental implant classification/recognition on any previous occasion, the above-mentioned results are very encouraging. GRAPHICAL ABSTRACT: [Image: see text] Springer Berlin Heidelberg 2022-08-18 2022 /pmc/articles/PMC9385426/ /pubmed/35978215 http://dx.doi.org/10.1007/s11517-022-02642-9 Text en © International Federation for Medical and Biological Engineering 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Article
Kohlakala, Aviwe
Coetzer, Johannes
Bertels, Jeroen
Vandermeulen, Dirk
Deep learning-based dental implant recognition using synthetic X-ray images
title Deep learning-based dental implant recognition using synthetic X-ray images
title_full Deep learning-based dental implant recognition using synthetic X-ray images
title_fullStr Deep learning-based dental implant recognition using synthetic X-ray images
title_full_unstemmed Deep learning-based dental implant recognition using synthetic X-ray images
title_short Deep learning-based dental implant recognition using synthetic X-ray images
title_sort deep learning-based dental implant recognition using synthetic x-ray images
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385426/
https://www.ncbi.nlm.nih.gov/pubmed/35978215
http://dx.doi.org/10.1007/s11517-022-02642-9
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