STS 2024 : 2nd Semi-supervised Teeth Segmentation MICCAI Challenge

📖 Challenge Publications

The STS Challenge was launched in 2023, with the aim of fostering the development of novel, robust teeth semi-supervised learning algorithms, and helping the developers with the evaluation of their new algorithmic developments. There are several publications related to the STS Challenge, including:

[1] Yaqi Wang*, Yifan Zhang, Xiaodiao Chen, Shuai Wang, Dahong Qian, Fan Ye, Feng Xu, Hongyuan Zhang, Qianni Zhang, Chengyu Wu, Yunxiang Li, Weiwei Cui, Shan Luo, Chengkai Wang, Tianhao Li, Yi Liu, Xiang Feng, Huiyu Zhou, Dongyun Liu, Qixuan Wang, Zhouhao Lin, Wei Song, Yuanlin Li, Bing Wang, Chunshi Wang, Qiupu Chen and Mingqian Li. STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation. Arxiv, 2024.

[2] Yifan Zhang, Fan Ye, Lingxiao Chen, Feng Xu, Xiaodiao Chen, Hongkun Wu, Mingguo Cao, Yunxiang Li, Yaqi Wang* and Xingru Huang*. Children's dental panoramic radiographs dataset for caries segmentation and dental disease detection. Scientific Data, 2023.

[3] Weiwei Cui, Yaqi Wang*, Qianni Zhang, Huiyu Zhou, Dan Song, Xingyong Zuo, Gangyong Jia and Liaoyuan Zeng*. CTooth: a fully annotated 3d dataset and benchmark for tooth volume segmentation on cone beam computed tomography images. International Conference on Intelligent Robotics and Applications, 2022.

[3] Weiwei Cui, Yaqi Wang*, Yilong Li, Dan Song, Xingyong Zuo, Jiaojiao Wang, Yifan Zhang, Huiyu Zhou, Bung san Chong, Liaoyuan Zeng and Qianni Zhang. CTooth+: A Large-Scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation. MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, 2022.

🙏 Acknowledgment

We appreciate the support from the National Natural Science Foundation of China (No.62206242) and China Science and Technology Foundation of Sichuan Province (No.2022YFS0116). We also extend our heartfelt gratitude to Hangzhou Dental Hospital Co. Ltd, Hangzhou Qiantang Dental Hospital, Communication University of Zhejiang for providing open datasets. In addition, We gratefully acknowledge the support of BASIC.AI, which offers an all-in-one smart data annotation platform.