Abstract
Automatic and precise tooth segmentation is crucial in computer aided dentistry, serving a pivotal role in various applications likes diagnosis and treatment planning. While prominent methods can attain satisfactory segmentation results, disproportion in the proportion of annotated images have an impact on model performance. In this paper, we present a cascade semi-supervised method named C-CPS for tooth segmentation, which designed for imbalanced datasets. C-CPS is built upon the idea that the model can acquire more essential information about uncertainty. It consists of two subtly different decoders and utilizes two distinct strategies for generating pseudo-labels. Particularly, one strategy aims at minimizing entropy and enhancing posterior probabilities, while the other constrains predictions by incorporating prior information. To boost the model training, we integrate the two generation strategies into a cycle supervision module. We evaluate C-CPS in MICCAI 2023 Challenge and respectively achieve 76.70% of Dice, 81.46% of IOU, and 16.42% normalized HD.
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Acknowledgements
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. The proposed solution is fully automatic without any manual intervention. We thank all the data owners for making the X-ray images and CT scans publicly available and Alibaba Cloud for hosting the challenge platform. This work is supported by Zhejiang Provincial Natural Science Foundation of China (LY22A010003), and National Natural Science Foundation of China (Grant No. 11801511).
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Lin, T., Lyu, P., Xiong, J., Wang, X., Song, K., Lou, Q. (2025). Prior-Aware Cross Pseudo Supervision for Semi-supervised Tooth Segmentation. 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_15
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