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一项关键任务，对精准诊断和治疗规划至关重要。尽管监督式深度学习方法可用于牙齿分割，但这类方法通常需要大量人工标注分割掩膜，耗时且成本高昂。本研究提出一种弱监督牙齿分割方法，以减少人工标注需求。该方法利用关键点检测网络输出的热力图和中间特征图来引导分割过程。我们创建了TriDental数据集（包含3000张标注牙齿关键点的口腔图像），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，可在无需显式分割标注的情况下实现精准的牙齿分割。检测到的关键点还可进一步用于优化分割掩膜。在TriDental数据集上的实验结果表明，与现有最先进分割方法相比，本方法在准确性和鲁棒性方面均具优势。该方法为真实牙科应用中的牙齿分割提供了高效低成本的解决方案，无需大量人工标注工作。