X-Match: A Semi-Supervised Framework for Oral Jawbones Segmentation Using Wavelet Transform for Enhanced Consistency Learning

Determining the occlusal position in CBCT images is a critical step in the digital virtual articulator treatment of Anterior Disc Displacement with Reduction (ADDwR). Current oral segmentation methods primarily rely on supervised learning, which typically requires a large dataset. However, acquiring oral datasets is complex and challenging, making semi-supervised learning more suitable. Current semi-supervised segmentation methods have several limitations. The perturbations used in consistency-based semi-supervised methods are often manually designed, which can introduce negative biases detrimental to training. Furthermore, semi-supervised learning often faces an empirical mismatch between labeled and unlabeled data. When these two data types are handled independently or without alignment, significant information derived from labeled data may not be fully utilized. We propose a novel semi-supervised framework X-Match for oral jawbones segmentation. The X-Match utilizes wavelet transforms to extract low-frequency and high-frequency information for consistency training, reducing the learning bias caused by manual perturbations. Furthermore, it combines labeled with unlabeled data bidirectionally during training, allowing unlabeled data to acquire comprehensive shared features from labeled data. Experimental results demonstrate that our method outperforms baselines and achieves superior performance in oral jawbones dataset.