  Accurate tooth identification and segmentation in Cone Beam Computed
Tomography (CBCT) dental images can significantly enhance the efficiency and
precision of manual diagnoses performed by dentists. However, existing
segmentation methods are mainly developed based on large data volumes training,
on which their annotations are extremely time-consuming. Meanwhile, the teeth
of each class in CBCT dental images being closely positioned, coupled with
subtle inter-class differences, gives rise to the challenge of indistinct
boundaries when training model with limited data. To address these challenges,
this study aims to propose a tasked-oriented Masked Auto-Encoder paradigm to
effectively utilize large amounts of unlabeled data to achieve accurate tooth
segmentation with limited labeled data. Specifically, we first construct a
self-supervised pre-training framework of masked auto encoder to efficiently
utilize unlabeled data to enhance the network performance. Subsequently, we
introduce a sparse masked prompt mechanism based on graph attention to
incorporate boundary information of the teeth, aiding the network in learning
the anatomical structural features of teeth. To the best of our knowledge, we
are pioneering the integration of the mask pre-training paradigm into the CBCT
tooth segmentation task. Extensive experiments demonstrate both the feasibility
of our proposed method and the potential of the boundary prompt mechanism.
