Automatic tooth segmentation for patients with alveolar clefts guided by tooth descriptors

Abstract

Tooth instance segmentation, which separates teeth from surrounding tissues and structures, provides the basis for preoperative planning and research. Existing methods have demonstrated breakthrough results on partial dental segmentation tasks. However, tooth instance segmentation based on cone-beam computed tomography in patients with alveolar clefts is still challenging due to tooth shape variation, high interdental similarity and adjacent tooth occlusion issues. In this study, we propose a new indicator called the tooth descriptor to assist in localizing tooth instances and guide segmentation tasks. The proposed two-stage network first predicts the tooth descriptors and the centroids used to generate the tooth cubes, and then the segmentation network extracts the complete tooth from the tooth cube guided by the morphology of the tooth descriptor. The experimental results demonstrate that the proposed algorithm exhibits excellent robustness in the task of segmenting CBCT (Cone-Beam Computed Tomography) tooth instances of patients with alveolar clefts, with a Dice accuracy of 94.4%, which outperforms state-of-the-art dental segmentation methods.