KEYWORDS: Dentistry, Teeth, Deep learning, Machine learning, Data modeling, Computer aided design, Point clouds, Visualization, Visual process modeling, Education and training
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
KEYWORDS: Teeth, Point clouds, Transformers, 3D modeling, Education and training, Reconstruction algorithms, 3D scanning, Design and modelling, Computer vision technology, Visual process modeling
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”
KEYWORDS: Teeth, Data modeling, 3D modeling, Machine learning, Clouds, 3D scanning, Neural networks, Control systems, Scientific research, Network architectures
Dental offices tackle thousands of dental reconstructions every year. Complexity and abnormalities in dentition make segmentation of an optical scan a challenging manual task that takes 45 minutes on average. The present work improves the generalization of currently available deep learning segmentation model on 3D dental arches by introducing a new loss function to leverage unlabeled available data. The semi-supervised segmentation network is trained using a joint loss that combines a supervised loss of annotated input and a self-supervised loss of non-labeled input. Our results showed that combining self-supervised and supervised learning improved the segmentation score by 13 % compared with purely supervised learning for the same amount of labeled data. It is concluded that combining representations obtained from self-supervised learning with supervised learning improves the generalization of the 3D tooth segmentation model in the case of few available labeled data.
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