A convolutional neural network for teeth margin detection on 3-dimensional dental meshes
This work addresses a domain-specific problem in dental imaging for improved margin detection, presenting an incremental improvement over baseline methods.
The authors tackled the problem of detecting teeth margins on 3D dental meshes by proposing a convolutional neural network for vertex classification, achieving an accuracy of 0.877 on test datasets.
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.