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Abstract
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.
Chunshi Wang and Bin Zhao contribute equally to this paper.
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Acknowledgments
The authors of this paper declare that the segmentation method they implemented for participation in the STS 2023 challenge has not used any pre-trained models nor additional datasets other than those provided by the organizers. Besides, this work is supported in part by the National Natural Science Foundation of China (Grant No.62076077), the Project of Improving the Basic Scientific Research Ability of Young and Middle-Aged Teachers in Universities of Guangxi Province (Grant No.2023KY0223), Youth Science Foundation of Guangxi Natural Science Foundation (Grant No.2023GXNSFBA026018) and the Guangxi Science and Technology Major Project (Grant No.AA22068057).
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Authors and Affiliations
School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, Guangxi, 541004, China
Chunshi Wang, Bin Zhao & Shuxue Ding
Guangxi Colleges and Universities Key Laboratory of AI Algorithm Engineering, Guilin, Guangxi, 541004, China
Bin Zhao & Shuxue Ding
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Correspondence toBin Zhao.
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Communication University of Zhejiang, Hangzhou, China
Yaqi Wang
Hangzhou Dianzi University, Hangzhou, China
Xiaodiao Chen
Shanghai Jiao Tong University, Shanghai, China
Dahong Qian
Hangzhou Dianzi University, Hangzhou, China
Fan Ye
Sichuan University, Chengdu, China
Shuai Wang
Shenzhen University, Shenzhen, China
Hongyuan Zhang
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Wang, C., Zhao, B., Ding, S. (2025). A Multi-stage Framework for 3D Individual Tooth Segmentation in Dental CBCT. In: Wang, Y., Chen, X., Qian, D., Ye, F., Wang, S., Zhang, H. (eds) Semi-supervised Tooth Segmentation. STS 2023. Lecture Notes in Computer Science, vol 14623. Springer, Cham. https://doi.org/10.1007/978-3-031-72396-4_4
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