TTNet : Three‐Stages Tooth Segmentation Network Based on Tooth Masks in CBCT Images

ABSTRACT Accurate identification and segmentation of teeth in cone‐beam computed tomography (CBCT) images are essential for dental diagnosis and treatment in digital dentistry. However, extracting regions of interest (ROI) from maxillofacial CBCT images remains difficult due to the low pixel ratio of tooth structures, especially in the apical area. Traditional tooth segmentation methods such as threshold‐based, region‐based, and edge‐based methods address limited accuracy under challenging imaging conditions. In this paper, we propose TTNNet, a three‐stage tooth instance segmentation network designed to improve tooth segmentation from CBCT images. The proposed TTNet employs an intersection‐based refinement of the tooth centroid heatmap, retaining only pixels that simultaneously lie within the predicted tooth mask and high‐probability heatmap regions. This intersection operation eliminates noise in the tooth centroid heatmap, such as regions that may have been labeled as teeth incorrectly. Extensive experiments on publicly available CBCT tooth dataset demonstrate that TTNet achieves superior performance compared to recent state‐of‐the‐art methods.