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In PloS one ; h5-index 176.0

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.

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