Patient Specific Classification of Dental Root Canal and Crown Shape

• Published in: Shape in Medical Imaging Vol. 12474; pp. 145 - 153
• Main Authors: Dumont, Maxime, Prieto, Juan Carlos, Brosset, Serge, Cevidanes, Lucia, Bianchi, Jonas, Ruellas, Antonio, Gurgel, Marcela, Massaro, Camila, Del Castillo, Aron Aliaga, Ioshida, Marcos, Yatabe, Marilia, Benavides, Erika, Rios, Hector, Soki, Fabiana, Neiva, Gisele, Aristizabal, Juan Fernando, Rey, Diego, Alvarez, Maria Antonia, Najarian, Kayvan, Gryak, Jonathan, Styner, Martin, Fillion-Robin, Jean-Christophe, Paniagua, Beatriz, Soroushmehr, Reza
• Format: Book Chapter Journal Article
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2020; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Deep learning; Dentistry; Shape analysis; Deep learning; Shape analysis; Dentistry
• ISBN: 3030610551; 9783030610555
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-61056-2_12

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.