Tooth Segmentation and Labeling from Digital Dental Casts

This paper presents an approach to automatic and accurate segmentation and identification of individual teeth from digital dental casts via deep graph convolutional neural networks. Instead of performing the teeth-gingiva and inter-tooth segmentation in two separate phases, the proposed method enables the simultaneous segmentation and identification of the gingiva and teeth. We perform the vertex-wise feature learning via the feature steered graph convolutional neural network (FeaStNet) [1] that dynamically updates the mapping between convolutional filters and local patches from digital dental casts. The proposed framework handles the tightly intertwined segmentation and labeling tasks with a novel constraint on crown shape distribution and concave contours to remove ambiguous labeling of neighboring teeth. We further enforce the smooth segmentation using the pairwise relationship in local patches to penalize rough and inaccurate region boundaries and regularize the vertex-wise labeling in the training process. The qualitative and quantitative evaluations on the digital dental casts obtained in the clinical orthodontics demonstrate that the proposed method achieves efficient and accurate tooth segmentation and produces performance improvements to the state-of-the-art.