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.

翻译：准确的牙齿识别与分割在锥形束计算机断层扫描（CBCT）口腔图像中可显著提升牙科医生手动诊断的效率与精确度。然而，现有分割方法主要基于大规模数据集训练，其标注过程极为耗时。同时，CBCT口腔图像中各类牙齿位置紧密相邻，加之类别间细微差异，导致在有限数据训练模型时面临边界模糊的挑战。为解决上述问题，本研究提出一种面向任务的掩码自编码器范式，以有效利用大量无标注数据，在有限标注条件下实现精准牙齿分割。具体而言，我们首先构建基于掩码自编码器的自监督预训练框架，高效利用无标注数据增强网络性能。随后，我们引入基于图注意力的稀疏掩码提示机制，融合牙齿边界信息，辅助网络学习牙齿解剖结构特征。据我们所知，本研究首次将掩码预训练范式整合至CBCT牙齿分割任务中。大量实验验证了所提方法的可行性及边界提示机制的潜力。