Research on intelligent tooth segmentation method combining multiple seed region growth and boundary extension_Journal of Biomedical Engineering

Authors：

LIU Zhihua ^1,2 ,  XUE Jiutao ^1,2 , TANG Hao ³ , LIAO Yuhe ^1,2

1. Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, Xi’an 710049, P. R. China;
2. Shaanxi Key Laboratory of Mechanical Product Quality Assurance and Diagnostics, Xi’an Jiaotong University, Xi'an 710049, P. R. China;
3. School of Mechanical and Power Engineering, Chongqing University of Science and Technology, Chongqing 401331, P. R. China;

Corresponding author：

XUE Jiutao, Email: xjt0224@stu.xjtu.edu.cn

Keywords：

Tooth segmentation; Regional growth strategy; Multi-seed points; Boundary extension

DOI：

10.7507/1001-5515.202309030

Video：

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Abstract Full text Figures/Tables Video References Cited by

The segmentation of dental models is a crucial step in computer-aided diagnosis and treatment systems for oral healthcare. To address the issues of poor universality and under-segmentation in tooth segmentation techniques, an intelligent tooth segmentation method combining multiple seed region growth and boundary extension is proposed. This method utilized the distribution characteristics of negative curvature meshes in teeth to obtain new seed points and effectively adapted to the structural differences between the top and sides of teeth through differential region growth. Additionally, the boundaries of the initial segmentation were extended based on geometric features, which was effectively compensated for under-segmentation issues in region growth. Ablation experiments and comparative experiments with current state-of-the-art algorithms demonstrated that the proposed method achieved better segmentation of crowded dental models and exhibited strong algorithm universality, thus possessing the capability to meet the practical segmentation needs in oral healthcare.

Citation： LIU Zhihua, XUE Jiutao, TANG Hao, LIAO Yuhe. Research on intelligent tooth segmentation method combining multiple seed region growth and boundary extension. Journal of Biomedical Engineering, 2024, 41(3): 520-526. doi: 10.7507/1001-5515.202309030 Copy

1.	Hao Jianye, Liao Ww, Zhang Yu, et al. Toward clinically applicable 3-dimensional tooth segmentation via deep learning. J Dent Res, 2021, 101(3): 304-311.
2.	Zhao Yue, Zhang Lingming, Yang Chongshi, et al. 3D Dental model segmentation with graph attentional convolution network. Pattern Recogn Lett, 2021, 152(C): 79-85.
3.	Louis B M D, Brosset S, Bianchi J, et al. A 3D surface segmentation framework. Spie Proceedings, 2021, 1159(6): 713-725.
4.	Sun Diya, Pei Yuru, Song Guanyin, et al. Tooth segmentation and labeling from digital dental casts// 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020). Iowa City: IEEE, 2020: 669-673.
5.	李占利, 孙志浩, 李洪安, 等. 图卷积网络下牙齿种子点自动选取. 中国图象图形学报, 2020, 25(7): 1481-1489.
6.	Wu T H, Lian C F, Lee S, et al. Two-stage mesh deep learning for automated tooth segmentation and landmark localization on 3D intraoral scans. IEEE T Med Imaging, 2022, 41(11): 3158-3166.
7.	Xu Xiaojie, Liu Chang, Zheng Youyi. 3D tooth segmentation and labeling using deep convolutional neural networks. IEEE T Vis Comput Gr, 2019, 25(7): 2336-2348.
8.	Zhao Yue, Zhang Lingming, Liu Yang, et al. Two-stream graph convolutional network for intra-oral scanner image segmentation. IEEE T Med Imaging, 2022, 41(4): 826-835.
9.	Zhou Zhengsong, Wan Hongli, Zhang Haoyu, et al. Segmentation of spontaneous intracerebral hemorrhage on ct with a region growing method based on watershed preprocessing. Front Neurol, 2022, 13: 157-172.
10.	Jiang Xiaotong. Coarse-to-fine 3d tooth segmentation via intuitive single clicks. Comput Graph-uk, 2022, 102: 601-609.
11.	姜晓通, 戴宁, 张长东, 等. 牙齿半自动精确分割算法. 计算机辅助设计与图形学学报, 2020, 32(5): 820-829.
12.	Kim S, Choi S. Automatic tooth segmentation of dental mesh using a transverse plane. IEEE Eng Med Biol Soc, 2018, 2018: 4122-4125.
13.	肖兵, 魏昕, 胡伟, 等. 基于特征线分段技术的牙齿分割算法. 计算机应用, 2017, 37(3): 844-848.
14.	Lacerda P, Gonzalez J, Rocha N, et al. A parallel method for anatomical structure segmentation based on 3d seeded region growing// 2020 International Joint Conference on Neural Networks (IJCNN). Glasgow: IEEE, 2020: 612-621.
15.	Lukas Z S W, Kohlhammer J, Köhm M. Combining seeded region growing and k-nearest neighbours for the segmentation of routinely acquired spatio-temporal image data. Int J Comput Ass Rad, 2023, 18(11): 2063-2072.
16.	Zhang Huahai, Bai Peiru, Min Xiaolin, et al. Hepatic vessel segmentation based on animproved 3D region growing algorithm. Journal of Physics: Conference Series, 2020, 1486(3): 32-38.
17.	Wang Monan, Li Donghui. An automatic segmentation method for lung tumor based on improved region growing algorithm. Diagnostics, 2022, 12(12): 2971-2982.
18.	范然, 钮叶新, 金小刚, 等. 计算机辅助牙齿隐形正畸系统. 计算机辅助设计与图形学学报, 2013, 25(1): 81-92.
19.	马勇, 柯永振, 杨帅. 基于网格抽取的牙齿模型快速分割算法. 计算机应用与软件, 2018, 35(5): 247-252.
20.	Zhang Y, Paik J, Koschan A, et al. A simple and efficient algorithm for part decomposition of 3d triangulated models based on curvature analysis// 2002 International Conference on Image Processing. Rochester: IEEE, 2002: 273-276.
21.	马天, 李赟, 李娇娇, 等. 基于目标区域约束的牙颌模型分割线探测方法. 系统仿真学报, 2022, 34(2): 376-384.
22.	Wu T H, Lian C, Piers C, et al. Deep learning for orthodontic CAD/CAM technologies. Machine Learning in Dentistry. Cham: Springer International Publishing. 2021: 117-129.
23.	Ben H A, Smaoui O, Chaabouni C H, et al. Teeth3DS: a benchmark for teeth segmentation and labeling from intra-oral 3D scans. arXiv, 2022, 2022: 2210.06094.

1. Hao Jianye, Liao Ww, Zhang Yu, et al. Toward clinically applicable 3-dimensional tooth segmentation via deep learning. J Dent Res, 2021, 101(3): 304-311.
2. Zhao Yue, Zhang Lingming, Yang Chongshi, et al. 3D Dental model segmentation with graph attentional convolution network. Pattern Recogn Lett, 2021, 152(C): 79-85.
3. Louis B M D, Brosset S, Bianchi J, et al. A 3D surface segmentation framework. Spie Proceedings, 2021, 1159(6): 713-725.
4. Sun Diya, Pei Yuru, Song Guanyin, et al. Tooth segmentation and labeling from digital dental casts// 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020). Iowa City: IEEE, 2020: 669-673.
5. 李占利, 孙志浩, 李洪安, 等. 图卷积网络下牙齿种子点自动选取. 中国图象图形学报, 2020, 25(7): 1481-1489.
6. Wu T H, Lian C F, Lee S, et al. Two-stage mesh deep learning for automated tooth segmentation and landmark localization on 3D intraoral scans. IEEE T Med Imaging, 2022, 41(11): 3158-3166.
7. Xu Xiaojie, Liu Chang, Zheng Youyi. 3D tooth segmentation and labeling using deep convolutional neural networks. IEEE T Vis Comput Gr, 2019, 25(7): 2336-2348.
8. Zhao Yue, Zhang Lingming, Liu Yang, et al. Two-stream graph convolutional network for intra-oral scanner image segmentation. IEEE T Med Imaging, 2022, 41(4): 826-835.
9. Zhou Zhengsong, Wan Hongli, Zhang Haoyu, et al. Segmentation of spontaneous intracerebral hemorrhage on ct with a region growing method based on watershed preprocessing. Front Neurol, 2022, 13: 157-172.
10. Jiang Xiaotong. Coarse-to-fine 3d tooth segmentation via intuitive single clicks. Comput Graph-uk, 2022, 102: 601-609.
11. 姜晓通, 戴宁, 张长东, 等. 牙齿半自动精确分割算法. 计算机辅助设计与图形学学报, 2020, 32(5): 820-829.
12. Kim S, Choi S. Automatic tooth segmentation of dental mesh using a transverse plane. IEEE Eng Med Biol Soc, 2018, 2018: 4122-4125.
13. 肖兵, 魏昕, 胡伟, 等. 基于特征线分段技术的牙齿分割算法. 计算机应用, 2017, 37(3): 844-848.
14. Lacerda P, Gonzalez J, Rocha N, et al. A parallel method for anatomical structure segmentation based on 3d seeded region growing// 2020 International Joint Conference on Neural Networks (IJCNN). Glasgow: IEEE, 2020: 612-621.
15. Lukas Z S W, Kohlhammer J, Köhm M. Combining seeded region growing and k-nearest neighbours for the segmentation of routinely acquired spatio-temporal image data. Int J Comput Ass Rad, 2023, 18(11): 2063-2072.
16. Zhang Huahai, Bai Peiru, Min Xiaolin, et al. Hepatic vessel segmentation based on animproved 3D region growing algorithm. Journal of Physics: Conference Series, 2020, 1486(3): 32-38.
17. Wang Monan, Li Donghui. An automatic segmentation method for lung tumor based on improved region growing algorithm. Diagnostics, 2022, 12(12): 2971-2982.
18. 范然, 钮叶新, 金小刚, 等. 计算机辅助牙齿隐形正畸系统. 计算机辅助设计与图形学学报, 2013, 25(1): 81-92.
19. 马勇, 柯永振, 杨帅. 基于网格抽取的牙齿模型快速分割算法. 计算机应用与软件, 2018, 35(5): 247-252.
20. Zhang Y, Paik J, Koschan A, et al. A simple and efficient algorithm for part decomposition of 3d triangulated models based on curvature analysis// 2002 International Conference on Image Processing. Rochester: IEEE, 2002: 273-276.
21. 马天, 李赟, 李娇娇, 等. 基于目标区域约束的牙颌模型分割线探测方法. 系统仿真学报, 2022, 34(2): 376-384.
22. Wu T H, Lian C, Piers C, et al. Deep learning for orthodontic CAD/CAM technologies. Machine Learning in Dentistry. Cham: Springer International Publishing. 2021: 117-129.
23. Ben H A, Smaoui O, Chaabouni C H, et al. Teeth3DS: a benchmark for teeth segmentation and labeling from intra-oral 3D scans. arXiv, 2022, 2022: 2210.06094.

Journal of Biomedical Engineering

Research on intelligent tooth segmentation method combining multiple seed region growth and boundary extension

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