Artificial Intelligence for Fast and Accurate 3-Dimensional Tooth Segmentation on Cone-beam Computed Tomography.

Journal: Journal of endodontics
PMID: 33434565

Abstract

INTRODUCTION: Tooth segmentation on cone-beam computed tomographic (CBCT) imaging is a labor-intensive task considering the limited contrast resolution and potential disturbance by various artifacts. Fully automated tooth segmentation cannot be achieved by merely relying on CBCT intensity variations. This study aimed to develop and validate an artificial intelligence (AI)-driven tool for automated tooth segmentation on CBCT imaging.

Authors

  • Pierre Lahoud
    OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven and Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven, Kapucijnenvoer 33, 3000, Leuven, Belgium.
  • Mostafa EzEldeen
    OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven and Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium; Department of Oral Health Sciences, KU Leuven and Paediatric Dentistry and Special Dental Care, University Hospitals Leuven, Leuven, Belgium. Electronic address: mostafa.ezeldeen@kuleuven.be.
  • Thomas Beznik
    Relu, Innovatie-en incubatiecentrum KU Leuven, Leuven, Belgium.
  • Holger Willems
    Relu, Innovatie-en incubatiecentrum KU Leuven, Leuven, 3000, Belgium.
  • Andre Leite
  • Adriaan Van Gerven
    Relu, R&D, 3000 Leuven, 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.

Keywords

External Resources

Popular Topics
Recent Journals