Automated dentition segmentation: 3D UNet-based approach with MIScnn framework.

Journal: Journal of the World federation of orthodontists
PMID: 39489636

Abstract

INTRODUCTION: Advancements in technology have led to the adoption of digital workflows in dentistry, which require the segmentation of regions of interest from cone-beam computed tomography (CBCT) scans. These segmentations assist in diagnosis, treatment planning, and research. However, manual segmentation is an expensive and labor-intensive process. Therefore, automated methods, such as convolutional neural networks (CNNs), provide a more efficient way to generate segmentations from CBCT scans.

Authors

  • Min Seok Kim
    School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea.
  • Elie Amm
    Department of Orthodontics and Dentofacial Orthopedics, Boston University Goldman School of Dentistry, Boston, Massachusetts.
  • Goli Parsi
    Department of Orthodontics and Dentofacial Orthopedics, Boston University Goldman School of Dentistry, Boston, Massachusetts.
  • Tarek ElShebiny
    Department of Orthodontics, Case Western Reserve University School of Dental Medicine, Cleveland, Ohio.
  • Melih Motro
    Department of Orthodontics and Dentofacial Orthopedics, Boston University Goldman School of Dentistry, Boston, Massachusetts.

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