A convolutional neural network for teeth margin detection on 3-dimensional dental meshes

TL;DR

This paper introduces a convolutional neural network designed for vertex classification on 3D dental meshes to accurately detect teeth margins, demonstrating improved performance over baseline models.

Contribution

The study presents a novel expanding layer in CNNs for 3D mesh vertex classification, enhancing teeth margin detection accuracy in dental analysis.

Findings

Best model achieved 87.7% accuracy on validation and test datasets.

Expanding layers outperformed baseline models in all tested configurations.

The approach effectively utilizes vertex features for precise dental margin detection.

Abstract

We proposed a convolutional neural network for vertex classification on 3-dimensional dental meshes, and used it to detect teeth margins. An expanding layer was constructed to collect statistic values of neighbor vertex features and compute new features for each vertex with convolutional neural networks. An end-to-end neural network was proposed to take vertex features, including coordinates, curvatures and distance, as input and output each vertex classification label. Several network structures with different parameters of expanding layers and a base line network without expanding layers were designed and trained by 1156 dental meshes. The accuracy, recall and precision were validated on 145 dental meshes to rate the best network structures, which were finally tested on another 144 dental meshes. All networks with our expanding layers performed better than baseline, and the best one achieved an accuracy of 0.877 both on validation dataset and test dataset.