Weakly Supervised Tooth Instance Segmentation on 3D Dental Models with Multi-Label Learning

Automatic tooth segmentation on 3D dental models is a fundamental task for computer-aided orthodontic treatment. Many deep learning methods aimed at precise tooth segmentation currently require meticulous point-wise annotations, which are extremely time-consuming and labor-intensive. To address this issue, we proposed a weakly supervised tooth instance segmentation network (WS-TIS), which only requires coarse class labels along with approximately 50% of point-wise tooth annotations. Our WS-TIS consists of two stages, including tooth discriminative localization and tooth instance segmentation. Precise tooth localization is frequently pivotal in instance segmentation. However, annotation of tooth centroids or bounding boxes is often challenging when we have limited point-wise tooth annotations. Therefore, we designed a proxy task to weakly supervise tooth localization. Specifically, we utilize a fine-grained multi-label classification task, equipping with the disentangled re-sampling strategy and a gated attention mechanism which can assist the network in learning discriminative tooth features. With discriminative features, certain feature visualization techniques can be easily employed to locate these discriminative regions, thereby accurately cropping out the teeth. In the second stage, a segmentation module was trained on limited annotated data (approximately 50% of all teeth) to accurately segment each tooth from cropping regions. Experiments on Teeth3DS demonstrate that our method with weakly supervised learning and weak annotations, achieves superior performance comparable to state-of-the-art approaches with full annotations.