Authors: Zhiming Cui (HKU); Bojun Zhang (Shanghai Jiao Tong University); Chunfeng Lian (Xi’an Jiaotong University); Changjian Li (University College London); LEI YANG (University of Hong Kong); Min Zhu (Shanghai Jiaotong University); Wenping Wang (The University of Hong Kong); Dinggang Shen (United Imaging Intelligence)
Abstract: 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 input CBCT images, 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.