Temporomandibular joint segmentation in MRI images using deep learning.

Journal: Journal of dentistry
PMID: 36368120

Abstract

OBJECTIVES: Temporomandibular joint (TMJ) internal derangements (ID) represent the most prevalent temporomandibular joint disorder (TMD) in the population and its diagnosis typically relies on magnetic resonance imaging (MRI). TMJ articular discs in MRIs usually suffer from low resolution and contrast, and it is difficult to identify them. In this study, we applied two convolutional neural networks (CNN) to delineate mandibular condyle, articular eminence, and TMJ disc in MRI images.

Authors

  • Mengxun Li
    School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Canada; Department of Prosthodontics, School of Stomatology, Wuhan University, China. Electronic address: mengxunli@whu.edu.cn.
  • Kumaradevan Punithakumar
  • Paul W Major
  • Lawrence H Le
  • Kim-Cuong T Nguyen
  • Camila Pacheco-Pereira
    School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Canada. Electronic address: cppereir@ualberta.ca.
  • Neelambar R Kaipatur
  • Brian Nebbe
    School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Canada. Electronic address: bnebbe@telusplanet.ne.
  • Jacob L Jaremko
    Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada.
  • Fabiana T Almeida
    School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Canada. Electronic address: fabiana@ualberta.ca.

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