Automatic segmentation of the temporomandibular joint disc on magnetic resonance images using a deep learning technique.

Journal: Dento maxillo facial radiology
PMID: 34347537

Abstract

OBJECTIVES: The aims of the present study were to construct a deep learning model for automatic segmentation of the temporomandibular joint (TMJ) disc on magnetic resonance (MR) images, and to evaluate the performances using the internal and external test data.

Authors

  • Michihito Nozawa
    Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
  • Hirokazu Ito
    Department of Nursing, Institute of Biomedical Sciences, Tokushima University, Tokushima, Japan.
  • Yoshiko Ariji
    Department of Oral Radiology, School of Dentistry, Osaka Dental University, Osaka, Japan.
  • Motoki Fukuda
    Department of Oral Radiology, School of Dentistry, Osaka Dental University, Osaka, Japan.
  • Chinami Igarashi
    Department of Oral and Maxillofacial Radiology, Tsurumi University School of Dentistry, Yokohama, Japan.
  • Masako Nishiyama
    Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
  • Nobumi Ogi
  • Akitoshi Katsumata
  • Kaoru Kobayashi
    Department of Oral and Maxillofacial Radiology, Tsurumi University School of Dentistry, Yokohama, Japan.
  • Eiichiro Ariji
    Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan.

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