A Multi-Stage Framework for 3D Individual Tooth Segmentation in Dental CBCT
This work addresses a domain-specific problem for dental diagnosis and treatment, with incremental improvements in semi-supervised segmentation.
The paper tackles the problem of 3D tooth segmentation in dental CBCT, addressing challenges like the need for large annotated datasets and domain shift, and reports achieving third place in the STS-3D challenge.
Cone beam computed tomography (CBCT) is a common way of diagnosing dental related diseases. Accurate segmentation of 3D tooth is of importance for the treatment. Although deep learning based methods have achieved convincing results in medical image processing, they need a large of annotated data for network training, making it very time-consuming in data collection and annotation. Besides, domain shift widely existing in the distribution of data acquired by different devices impacts severely the model generalization. To resolve the problem, we propose a multi-stage framework for 3D tooth segmentation in dental CBCT, which achieves the third place in the "Semi-supervised Teeth Segmentation" 3D (STS-3D) challenge. The experiments on validation set compared with other semi-supervised segmentation methods further indicate the validity of our approach.