| Literature DB >> 33385170 |
Maxime Dumont1, Juan Carlos Prieto2, Serge Brosset1, Lucia Cevidanes1, Jonas Bianchi1, Antonio Ruellas1, Marcela Gurgel1, Camila Massaro1, Aron Aliaga Del Castillo1, Marcos Ioshida1, Marilia Yatabe1, Erika Benavides1, Hector Rios1, Fabiana Soki1, Gisele Neiva1, Juan Fernando Aristizabal3, Diego Rey4, Maria Antonia Alvarez4, Kayvan Najarian1, Jonathan Gryak1, Martin Styner2, Jean-Christophe Fillion-Robin5, Beatriz Paniagua5, Reza Soroushmehr1.
Abstract
This paper proposes machine learning approaches to support dentistry researchers in the context of integrating imaging modalities to analyze the morphology of tooth crowns and roots. One of the challenges to jointly analyze crowns and roots with precision is that two different image modalities are needed. Precision in dentistry is mainly driven by dental crown surfaces characteristics, but information on tooth root shape and position is of great value for successful root canal preparation, pulp regeneration, planning of orthodontic movement, restorative and implant dentistry. An innovative approach is to use image processing and machine learning to combine crown surfaces, obtained by intraoral scanners, with three dimensional volumetric images of the jaws and teeth root canals, obtained by cone beam computed tomography. In this paper, we propose a patient specific classification of dental root canal and crown shape analysis workflow that is widely applicable.Entities:
Keywords: Deep learning; Dentistry; Shape analysis
Year: 2020 PMID: 33385170 PMCID: PMC7773155 DOI: 10.1007/978-3-030-61056-2_12
Source DB: PubMed Journal: Shape Med Imaging (2020)