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- W4304701124 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." @default.
- W4304701124 created "2022-10-12" @default.
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- W4304701124 date "2022-10-12" @default.
- W4304701124 modified "2023-10-10" @default.
- W4304701124 title "Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR" @default.
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- W4304701124 doi "https://doi.org/10.1371/journal.pone.0275033" @default.
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