Deep learning for automated segmentation of the temporomandibular joint.

Journal: Journal of dentistry
PMID: 36870441

Abstract

OBJECTIVE: Quantitative analysis of the volume and shape of the temporomandibular joint (TMJ) using cone-beam computed tomography (CBCT) requires accurate segmentation of the mandibular condyles and the glenoid fossae. This study aimed to develop and validate an automated segmentation tool based on a deep learning algorithm for accurate 3D reconstruction of the TMJ.

Authors

  • Shankeeth Vinayahalingam
    Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
  • Bo Berends
    Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, Postal number 590, Nijmegen, HB 6500, The Netherlands; Radboudumc 3DLab, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
  • Frank Baan
    Radboudumc 3D Lab, Radboud University Nijmegen Medical Centre, Geert Grooteplein Zuid 10, 6525, GA, Nijmegen, the Netherlands. Electronic address: Frank.Baan@radboudumc.nl.
  • David Anssari Moin
    Promaton Co. Ltd., Amsterdam 1076 GR, the Netherlands.
  • Rik van Luijn
    Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, Postal number 590, Nijmegen, HB 6500, The Netherlands.
  • Stefaan BergĂ©
    Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
  • Tong Xi
    Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. Tong.Xi@radboudumc.nl.

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