Intra-oral scan segmentation using deep learning.

Journal: BMC oral health
PMID: 37670290

Abstract

OBJECTIVE: Intra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments, which require teeth segmentations with high accuracy and resolution. Manual teeth segmentation, the gold standard up until now, is time-consuming, tedious, and observer-dependent. This study aims to develop an automated teeth segmentation and labeling system using deep learning.

Authors

  • Shankeeth Vinayahalingam
    Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
  • Steven Kempers
    Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands; Department of Artificial Intelligence, Radboud University, Nijmegen, the Netherlands.
  • Julian Schoep
    Promaton Co. Ltd., Amsterdam 1076 GR, the Netherlands.
  • Tzu-Ming Harry Hsu
    MIT Computer Science & Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA, 02139, USA.
  • David Anssari Moin
    Promaton Co. Ltd., Amsterdam 1076 GR, the Netherlands.
  • Bram van Ginneken
    Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; Fraunhofer Mevis, Bremen, Germany.
  • Tabea Flügge
    Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and HumboldtUniversität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany.
  • Marcel Hanisch
    Promaton Co. Ltd., Amsterdam 1076 GR, the Netherlands; Department of Oral and Maxillofacial Surgery, Universitätsklinikum Münster, Münster, Germany.
  • Tong Xi
    Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. Tong.Xi@radboudumc.nl.

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