This article published in the Mathematics and Computers in Simulation journal from Elsevier by our researchers Ammar Alsheghi, Ying Zhang, Farnoosh Ghadiri , Julia Keren, Farida Cheriet and Francois Guilbault, talks about mesh-based segmentation for automated margin line generation on incisors receiving crown treatment.
Abstract
Dental crowns are essential dental treatments for restoring damaged or missing teeth of patients. Recent design approaches of dental crowns are carried out using commercial dental design software. Once a scan of a preparation is uploaded to the software, a dental technician needs to manually define a precise margin line on the preparation surface which constitutes a nonrepeatable and inconsistent procedure. This work proposes a new framework to determine margin lines automatically and accurately using deep learning. A dataset of incisor teeth was provided by a collaborating dental laboratory to train a deep learning segmentation model. A mesh-based neural network was modified by changing its input channels and used to segment the prepared tooth in two regions such that the margin line is contained within the boundary faces separating the two regions. Next, k-fold cross-validation was used to train 5 models and a voting classifier technique was used to combine their results to enhance the segmentation. After that, boundary smoothening and optimization using the graph cut method was applied to refine the segmentation results. Then, boundary faces separating the two regions were selected to represent the margin line faces. A spline was approximated to best fit the centers of the boundary faces to predict the margin line. Our results show that an ensemble model combined with maximum probability predicted the highest number of successful test cases (7 out of 13) based on a maximum distance threshold of 200 μm (representing human error) between the predicted and ground truth point clouds. It was also demonstrated that the better the quality of the preparation, the smaller the divergence between the predicted and ground truth margin lines (Spearman’s rank correlation coefficient of -0.683). We provide the train and test datasets for the community
You may read the full publication here or download the pdf version.
