3D generation of dental crown bottoms using context learning

Imane Chafi; Farida Cheriet; Julia Keren; Ying Zhang; François Guibault

doi:10.1117/12.3006955

2 April 2024 3D generation of dental crown bottoms using context learning

Imane Chafi, Farida Cheriet, Julia Keren, Ying Zhang, François Guibault

Author Affiliations +

Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 129310I (2024) https://doi.org/10.1117/12.3006955
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

The generation of valid and realistic dental crown bottoms plays a central role in dentistry, as dental crown bottoms are the first point of contact between a tooth preparation and its crown. Every tooth is different, and the retention of the crown bottom heavily depends on how well it fits the preparation while conserving essential properties for ceramic adhesion and smoothness. From this, the generation of the crown bottom becomes a difficult task that only qualified individuals such as dental technicians can complete. Standard geometric modelling techniques such as Computer-Aided Design (CAD) software programs have since been used for this purpose, providing a reliable basis for the generation of dental crown bottoms. Conversely, recent improvements in deep learning have presented new avenues in shape generation tasks that allow for personalized shapes to be created in a short period of time based on learned context. Starting from a set of preparation shapes, this project seeks to compare the efficacy of automatic geometric techniques to deep learning methods in the framework of dental crown bottom shape generation. Results show that deep learning methods such as GANs demand no human manipulation and provide similar visual results to the geometric model on unseen cases in an unsupervised manner. Our code is available at https://github.com/ImaneChafi/C.B.GEN and https://github.com/ImaneChafi/Prep-GAN

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Imane Chafi, Farida Cheriet, Julia Keren, Ying Zhang, and François Guibault "3D generation of dental crown bottoms using context learning", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 129310I (2 April 2024); https://doi.org/10.1117/12.3006955

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE