USCT: Uncertainty-regularized symmetric consistency learning for semi-supervised teeth segmentation in CBCT

Automatic and accurate segmentation of teeth from CBCT is of great importance for dental diagnosis and treatment. In this paper, we propose a symmetric consistent semi-supervised segmentation network by uncertainty regularization that enables accurate segmentation of teeth with limited labeled data. Specifically, a two-stage segmentation framework is developed, where the input CBCT images are first passed through a coarse simple network to obtain the bounding box of the crown region, which is then fed into the proposed network to obtain a refined segmentation probability map. In particular, in place of the commonly used residual skip connection, we use Adaptive Channel Interaction Module (ACIM) between the encoder and decoder. Incremental feature fusion operations are performed in a left-to-right and bottom-to-top order to enhance feature reusing. To effectively utilize the unlabeled data, we combine the consistency between the pixel-by-pixel segmentation task and the level-set regression task. We use the average feature map as a new cue to compute the uncertainty of each layer as a regularization constraint term for predicting the segmentation. Experiments on the CBCT dental 3D models provided by Fudan dual-modality dental imaging (FDDI) dataset show that the proposed method outperforms existing semi-supervised learning methods in all five metrics considered, which demonstrates the effectiveness of the novel pipeline proposed in this work for the dental segmentation task.