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

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          Abstract

          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.

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          Author and article information

          Journal
          Journal ID (iso-abbrev): Dentomaxillofac Radiol
          Title: Dento maxillo facial radiology
          Publisher: British Institute of Radiology
          ISSN: 0250-832X
          ISSN (Print): 0250-832X
          Publication date (Electronic): Jan 01 2022
          Volume: 51
          Issue: 1
          Affiliations
          [1 ] Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan.
          [2 ] Division of Radiology, Department of Oral Diagnostic Sciences, Showa University School of Dentistry, Tokyo, Japan.
          [3 ] Department of Oral and Maxillofacial Radiology, Tsurumi University School of Dentistry, Yokohama, Japan.
          [4 ] Department of Oral and Maxillofacial Surgery, Aichi Gakuin University School of Dentistry, Nagoya, Japan.
          [5 ] Department of Oral Radiology, Asahi University School of Dentistry, Mizuho, Japan.
          Article
          DOI: 10.1259/dmfr.20210185
          PMC ID: 8693319
          PubMed ID: 34347537
          SO-VID: 526a16b2-68e8-43a7-8944-ece7aab96477
          History

          Keywords: Temporomandibular joint disc,Artificial intelligence,Deep learning,Magnetic resonance imaging
          Data availability:
          Keywords: Temporomandibular joint disc, Artificial intelligence, Deep learning, Magnetic resonance imaging

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