Improving the quality of dental crown using a transformer-based method

Golriz Hosseinimanesh; Farnoosh Ghadiri; Ammar Alsheghri; Ying Zhang; Julia Keren; Farida Cheriet; Francois Guibault

doi:10.1117/12.2654350

7 April 2023 Improving the quality of dental crown using a transformer-based method

Golriz Hosseinimanesh, Farnoosh Ghadiri, Ammar Alsheghri, Ying Zhang, Julia Keren, Farida Cheriet, Francois Guibault

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124633R (2023) https://doi.org/10.1117/12.2654350
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Designing a synthetic crown is a time-consuming, inconsistent, and labor-intensive process. In this work, we present a fully automatic method that not only learns human design dental crowns, but also improves the consistency, functionality, and esthetic of the crowns. Following success in point cloud completion using the transformer-based network, we tackle the problem of the crown generation as a point-cloud completion around a prepared tooth. To this end, we use a geometry-aware transformer to generate dental crowns. Our main contribution is to add a margin line information to the network, as the accuracy of generating a precise margin line directly, determines whether the designed crown and prepared tooth are closely matched to allow appropriate adhesion. Using our ground truth crown, we can extract the margin line as a spline and sample the spline into 1000 points. We feed the obtained margin line along with two neighbor teeth of the prepared tooth and three closest teeth in the opposing jaw. We also add the margin line points to our ground truth crown to increase the resolution at the margin line. Our experimental results show an improvement in the quality of the designed crown when considering the actual context composed of the prepared tooth along with the margin line compared with a crown generated in an empty space as was done by other studies in the literature. Our code is available at : “https://github.com/Golriz-code/shellGeneration/tree/main/Shell%20Generation”

Citation Download Citation

Golriz Hosseinimanesh, Farnoosh Ghadiri, Ammar Alsheghri, Ying Zhang, Julia Keren, Farida Cheriet, and Francois Guibault "Improving the quality of dental crown using a transformer-based method", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124633R (7 April 2023); https://doi.org/10.1117/12.2654350

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