Fully automatic segmentation of sinonasal cavity and pharyngeal airway based on convolutional neural networks.

Journal: American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
Published Date: Jun 1, 2021

Abstract

INTRODUCTION: This study aimed to test the accuracy of a new automatic deep learning-based approach on the basis of convolutional neural networks (CNN) for fully automatic segmentation of the sinonasal cavity and the pharyngeal airway from cone-beam computed tomography (CBCT) scans.

Authors

  • Rosalia Leonardi
    Department of Orthodontics, School of Dentistry, University of Catania, Catania, Italy. Electronic address: rleonard@unict.it.
  • Antonino Lo Giudice
    Department of Orthodontics, School of Dentistry, University of Catania, Catania, Italy.
  • Marco Farronato
    Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Department of Biomedical, Surgical and Dental Sciences, School of Dentistry, University of Milan, Milan, Italy.
  • Vincenzo Ronsivalle
    Department of Orthodontics, School of Dentistry, University of Catania, Catania, Italy.
  • Silvia Allegrini
    Private practice, Pisa, Italy.
  • Giuseppe Musumeci
    Department of Biomedical and Biotechnological Sciences, Human Anatomy and Histology Section, School of Medicine, University of Catania, Catania, Italy.
  • Concetto Spampinato
    Department of Computer and Telecommunications Engineering, University of Catania, Catania, Italy.

Keywords

External Resources

Popular Topics
Recent Journals