The article discusses a new approach to teeth segmentation in dental images using a weakly supervised learning method. This method reduces the need for extensive manual annotation, which is often time-consuming and costly. It uses output heatmaps and intermediate feature maps from a keypoint detection network to guide the segmentation process. The researchers introduced the TriDental dataset, consisting of 3000 oral cavity images annotated with teeth keypoints, to train a teeth keypoint detection network. The experimental results demonstrated the superiority of this approach in terms of accuracy and robustness compared to traditional segmentation methods, providing a cost-effective and efficient solution for teeth segmentation in real-world dental applications.
Publication date: 14 Nov 2023
Project Page: Not Specified
Paper: https://arxiv.org/pdf/2311.07398
