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.

使用弱监督方法进行牙齿分割，通过关键点检测网络的输出热图和中间特征图来引导分割过程，采用 TriDental 数据集训练牙齿关键点检测网络，结合不同层次的特征图实现准确牙齿分割，进一步利用检测到的关键点优化分割结果，在真实牙科应用中实现成本效益高且高效的牙齿分割。