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

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 r

SUBMITTER: Gillot M 

PROVIDER: S-EPMC9555672 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature