A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study.

Journal: Journal of dentistry
Published Date: Oct 26, 2021

Abstract

OBJECTIVES: Automatic tooth segmentation and classification from cone beam computed tomography (CBCT) have become an integral component of the digital dental workflows. Therefore, the aim of this study was to develop and validate a deep learning approach for an automatic tooth segmentation and classification from CBCT images.

Authors

  • Eman Shaheen
    OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer 33, Leuven, Belgium.
  • Andre Leite
  • Khalid Ayidh Alqahtani
    OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Kapucijnenvoer 33, BE-3000 Leuven, Belgium; Department of Oral and Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
  • Andreas Smolders
    Relu BV, Kapeldreef 60, BE-3001, Leuven, Belgium.
  • Adriaan Van Gerven
    Relu, R&D, 3000 Leuven, Belgium.
  • Holger Willems
    Relu, Innovatie-en incubatiecentrum KU Leuven, Leuven, 3000, Belgium.
  • Reinhilde Jacobs
    OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven and Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven, Kapucijnenvoer 7, 3000, Leuven, Belgium; Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden. Electronic address: reinhilde.jacobs@ki.se.

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