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Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR

The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segment...

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Autores principales: Gillot, Maxime, Baquero, Baptiste, Le, Celia, Deleat-Besson, Romain, Bianchi, Jonas, Ruellas, Antonio, Gurgel, Marcela, Yatabe, Marilia, Al Turkestani, Najla, Najarian, Kayvan, Soroushmehr, Reza, Pieper, Steve, Kikinis, Ron, Paniagua, Beatriz, Gryak, Jonathan, Ioshida, Marcos, Massaro, Camila, Gomes, Liliane, Oh, Heesoo, Evangelista, Karine, Chaves Junior, Cauby Maia, Garib, Daniela, Costa, Fábio, Benavides, Erika, Soki, Fabiana, Fillion-Robin, Jean-Christophe, Joshi, Hina, Cevidanes, Lucia, Prieto, Juan Carlos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555672/
https://www.ncbi.nlm.nih.gov/pubmed/36223330
http://dx.doi.org/10.1371/journal.pone.0275033
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author Gillot, Maxime
Baquero, Baptiste
Le, Celia
Deleat-Besson, Romain
Bianchi, Jonas
Ruellas, Antonio
Gurgel, Marcela
Yatabe, Marilia
Al Turkestani, Najla
Najarian, Kayvan
Soroushmehr, Reza
Pieper, Steve
Kikinis, Ron
Paniagua, Beatriz
Gryak, Jonathan
Ioshida, Marcos
Massaro, Camila
Gomes, Liliane
Oh, Heesoo
Evangelista, Karine
Chaves Junior, Cauby Maia
Garib, Daniela
Costa, Fábio
Benavides, Erika
Soki, Fabiana
Fillion-Robin, Jean-Christophe
Joshi, Hina
Cevidanes, Lucia
Prieto, Juan Carlos
author_facet Gillot, Maxime
Baquero, Baptiste
Le, Celia
Deleat-Besson, Romain
Bianchi, Jonas
Ruellas, Antonio
Gurgel, Marcela
Yatabe, Marilia
Al Turkestani, Najla
Najarian, Kayvan
Soroushmehr, Reza
Pieper, Steve
Kikinis, Ron
Paniagua, Beatriz
Gryak, Jonathan
Ioshida, Marcos
Massaro, Camila
Gomes, Liliane
Oh, Heesoo
Evangelista, Karine
Chaves Junior, Cauby Maia
Garib, Daniela
Costa, Fábio
Benavides, Erika
Soki, Fabiana
Fillion-Robin, Jean-Christophe
Joshi, Hina
Cevidanes, Lucia
Prieto, Juan Carlos
author_sort Gillot, Maxime
collection PubMed
description The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians. This work focuses on the integration of the state-of-the-art UNEt TRansformers (UNETR) of the Medical Open Network for Artificial Intelligence (MONAI) framework. We trained and tested our models using 618 de-identified Cone-Beam Computed Tomography (CBCT) volumetric images of the head acquired with several parameters from different centers for a generalized clinical application. Our results on a 5-fold cross-validation showed high accuracy and robustness with a Dice score up to 0.962±0.02. Our code is available on our public GitHub repository.
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spelling pubmed-95556722022-10-13 Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR Gillot, Maxime Baquero, Baptiste Le, Celia Deleat-Besson, Romain Bianchi, Jonas Ruellas, Antonio Gurgel, Marcela Yatabe, Marilia Al Turkestani, Najla Najarian, Kayvan Soroushmehr, Reza Pieper, Steve Kikinis, Ron Paniagua, Beatriz Gryak, Jonathan Ioshida, Marcos Massaro, Camila Gomes, Liliane Oh, Heesoo Evangelista, Karine Chaves Junior, Cauby Maia Garib, Daniela Costa, Fábio Benavides, Erika Soki, Fabiana Fillion-Robin, Jean-Christophe Joshi, Hina Cevidanes, Lucia Prieto, Juan Carlos PLoS One Research Article The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians. This work focuses on the integration of the state-of-the-art UNEt TRansformers (UNETR) of the Medical Open Network for Artificial Intelligence (MONAI) framework. We trained and tested our models using 618 de-identified Cone-Beam Computed Tomography (CBCT) volumetric images of the head acquired with several parameters from different centers for a generalized clinical application. Our results on a 5-fold cross-validation showed high accuracy and robustness with a Dice score up to 0.962±0.02. Our code is available on our public GitHub repository. Public Library of Science 2022-10-12 /pmc/articles/PMC9555672/ /pubmed/36223330 http://dx.doi.org/10.1371/journal.pone.0275033 Text en © 2022 Gillot et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Gillot, Maxime
Baquero, Baptiste
Le, Celia
Deleat-Besson, Romain
Bianchi, Jonas
Ruellas, Antonio
Gurgel, Marcela
Yatabe, Marilia
Al Turkestani, Najla
Najarian, Kayvan
Soroushmehr, Reza
Pieper, Steve
Kikinis, Ron
Paniagua, Beatriz
Gryak, Jonathan
Ioshida, Marcos
Massaro, Camila
Gomes, Liliane
Oh, Heesoo
Evangelista, Karine
Chaves Junior, Cauby Maia
Garib, Daniela
Costa, Fábio
Benavides, Erika
Soki, Fabiana
Fillion-Robin, Jean-Christophe
Joshi, Hina
Cevidanes, Lucia
Prieto, Juan Carlos
Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR
title Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR
title_full Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR
title_fullStr Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR
title_full_unstemmed Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR
title_short Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR
title_sort automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3d unetr
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555672/
https://www.ncbi.nlm.nih.gov/pubmed/36223330
http://dx.doi.org/10.1371/journal.pone.0275033
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