Hierarchical Morphology-Guided Tooth Instance Segmentation from CBCT Images

Automatic and accurate segmentation of individual teeth, i.e., tooth instance segmentation, from CBCT images is an essential step for computer-aided dentistry. Previous works typically overlooked rich morphological features of teeth, such as tooth root apices, critical for successful treatment outcomes. This paper presents a two-stage learning-based framework that explicitly leverages the comprehensive geometric guidance provided by a hierarchical tooth morphological representation for tooth instance segmentation. Given a 3D input CBCT image, our method first learns to extract the tooth centroids and skeletons for identifying each tooth's rough position and topological structures, respectively. Based on the outputs of the first step, a multi-task learning mechanism is further designed to estimate each tooth's volumetric mask by simultaneously regressing boundary and root apices as auxiliary tasks. Extensive evaluations, ablation studies, and comparisons with existing methods show that our approach achieved state-of-the-art segmentation performance, especially around the challenging dental parts (i.e., tooth roots and boundaries). These results suggest the potential applicability of our framework in real-world clinical scenarios.