Sparse Anatomical Prompt Semi-Supervised Learning with Masked Image Modeling for CBCT Tooth Segmentation

Accurate tooth identification and segmentation in Cone Beam Computed Tomography (CBCT) dental images can significantly enhance the efficiency and precision of manual diagnoses performed by dentists. However, existing segmentation methods are mainly developed based on large data volumes training, on which their annotations are extremely time-consuming. Meanwhile, the teeth of each class in CBCT dental images being closely positioned, coupled with subtle inter-class differences, gives rise to the challenge of indistinct boundaries when training model with limited data. To address …

annotations cs.cv data dental efficiency identification image images modeling precision prompt segmentation semi-supervised semi-supervised learning supervised learning training