Center-Sensitive and Boundary-Aware Tooth Instance Segmentation and Classification from Cone-Beam CT

Tooth instance segmentation provides important assistance for computer-aided orthodontic treatment. Many previous studies on this issue have limited performance on distinguishing adjacent teeth and obtaining accurate tooth boundaries. To address the challenging task, in this paper, we present a novel method achieving tooth instance segmentation and classification from cone beam CT (CBCT) images. The core of our method is a two-level hierarchical deep neural network. We first embed center-sensitive mechanism with global stage heatmap, so as to ensure accurate tooth centers and guide the localization of tooth instances. Then in the local stage, DenseASPP-UNet is proposed for fine segmentation and classification of individual tooth. Further, in order to improve the accuracy of tooth segmentation boundary and refine the boundaries of overlapped teeth, a boundary-aware dice loss and a novel label optimization are also applied in our method. Comparative experiments show that the proposed framework exhibits high segmentation performance and outperforms the state-of-the-art methods.