Publications
2022
M, Gillot; B, Baquero; C, Le; Deleat-Besson, R; J, Bianchi; M, Gurgel; M, Yatabe; N, Al Turkestani; K, Najarian
Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans during 3D UNETR Journal Article
In: PLoS One, vol. 17, iss. 10, 2022.
Abstract | Links | BibTeX | Tags: 3d, CBCT, Cone-beam computed tomography (CBCT), multi-anatomical skull structure, structure segmentation
@article{Bianchi2022b,
title = {Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans during 3D UNETR},
author = {Gillot M and Baquero B and Le C and R Deleat-Besson and Bianchi J and Gurgel M and Yatabe M and Al Turkestani N and Najarian K},
url = {https://pubmed.ncbi.nlm.nih.gov/36223330/},
doi = {10.1371/journal.pone.0275033},
year = {2022},
date = {2022-10-12},
journal = {PLoS One},
volume = {17},
issue = {10},
abstract = {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.},
keywords = {3d, CBCT, Cone-beam computed tomography (CBCT), multi-anatomical skull structure, structure segmentation},
pubstate = {published},
tppubtype = {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.
M, Gillot; B, Baquero; C, Le; Deleat-Besson, R; J, Bianchi; M, Gurgel; M, Yatabe; N, Al Turkestani; K, Najarian
Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans during 3D UNETR Journal Article
In: PLoS One, vol. 17, iss. 10, 2022.
@article{Bianchi2022b,
title = {Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans during 3D UNETR},
author = {Gillot M and Baquero B and Le C and R Deleat-Besson and Bianchi J and Gurgel M and Yatabe M and Al Turkestani N and Najarian K},
url = {https://pubmed.ncbi.nlm.nih.gov/36223330/},
doi = {10.1371/journal.pone.0275033},
year = {2022},
date = {2022-10-12},
journal = {PLoS One},
volume = {17},
issue = {10},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {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.
2022 |
M, Gillot; B, Baquero; C, Le; Deleat-Besson, R; J, Bianchi; M, Gurgel; M, Yatabe; N, Al Turkestani; K, Najarian: Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans during 3D UNETR. In: PLoS One, vol. 17, iss. 10, 2022. (Type: Journal Article | Abstract | Links | BibTeX | Tags: 3d, CBCT, Cone-beam computed tomography (CBCT), multi-anatomical skull structure, structure segmentation)@article{Bianchi2022b,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. |