Boundary-Constrained Graph Network for Tooth Segmentation on 3D Dental Surfaces

Yuwen Tan, Xiang Xiang

Abstract: Accurate tooth segmentation on 3D dental models is an important task in computer-aided dentistry. In recent years, several deep learning-based methods have been proposed for automatic tooth segmentation. However, previous tooth segmentation methods often face challenges in accurately delineating boundaries, leading to a decline in overall segmentation performance. In this paper, we propose a boundary-constrained graph-based neural network that establishes the connectivity of mesh cells based on feature distances and utilizes several modules to encode local regions. To enhance segmentation performance in tooth-gingiva boundary regions, we integrate an auxiliary loss to segment the tooth and gingiva. Furthermore, to improve the performance in tooth-tooth boundary regions, we introduce a contrastive boundary-constrained loss that specifically enhances the distinctiveness of features within boundary mesh cells. Following the network prediction, we apply a post-processing step based on the graph cut to refine the boundaries. Experimental results demonstrate that our method achieves state-of-the-art performance in 3D tooth segmentation.