Teeth segmentation is an essential task in dental image analysis for accurate diagnosis and treatment planning. While supervised deep learning methods can be utilized for teeth segmentation, they often require extensive manual annotation of segmentation masks, which is time-consuming and costly. In this research, we propose a weakly supervised approach for teeth segmentation that reduces the need for manual annotation. Our method utilizes the output heatmaps and intermediate feature maps from a keypoint detection network to guide the segmentation process. We introduce the TriDental dataset, consisting of 3000 oral cavity images annotated with teeth keypoints, to train a teeth keypoint detection network. We combine feature maps from different layers of the keypoint detection network, enabling accurate teeth segmentation without explicit segmentation annotations. The detected keypoints are also used for further refinement of the segmentation masks. Experimental results on the TriDental dataset demonstrate the superiority of our approach in terms of accuracy and robustness compared to state-of-the-art segmentation methods. Our method offers a cost-effective and efficient solution for teeth segmentation in real-world dental applications, eliminating the need for extensive manual annotation efforts.

翻译：牙齿分割是牙科图像分析中进行准确诊断和治疗规划的关键任务。尽管监督深度学习方法可用于牙齿分割，但其通常需要大量手动标注分割掩膜，这一过程既耗时又成本高昂。本研究提出一种弱监督的牙齿分割方法，可减少对手动标注的依赖。该方法利用关键点检测网络输出的热力图及中间特征图来引导分割过程。我们引入TriDental数据集（包含3000张口腔图像及牙齿关键点标注），用于训练牙齿关键点检测网络。通过融合关键点检测网络不同层的特征图，该方法无需显式分割标注即可实现精准的牙齿分割。检测到的关键点还可用于进一步优化分割掩膜。在TriDental数据集上的实验结果表明，与现有最先进的分割方法相比，本方法在准确性和鲁棒性方面均具有优越性。该方法为实际牙科应用提供了一种经济高效的分割解决方案，避免了大量手动标注工作。