Oral panorama reconstruction method based on pre-segmentation and Bezier function_Journal of Biomedical Engineering

Authors：

HOU Changpeng ¹ ,  ZHU Fudong ² , ZHANG Gaohua ¹ , LYU Zhen ³ , LIU Yunfeng ⁴ , ZHU Weidong ¹

1. Zhejiang University, College of Mechanical Engineering, Hangzhou 310058, P. R. China;
2. Affiliated Stomatological Hospital of Zhejiang University School of Medicine, Hangzhou 310006, P. R. China;
3. Hangzhou City University, Engineering college, HangZhou 310015, P. R. China;
4. Zhejiang University of Technology, College of Mechanical Engineering, Hangzhou 310023, P. R. China;

Corresponding author：

ZHU Fudong, Email: zfd@zju.edu.cn

Keywords：

Oral panorama images; Dental arch lines; Cone beam computerized tomography; Bezier curve; Deep learning

DOI：

10.7507/1001-5515.202302036

Video：

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Abstract Full text Figures/Tables Video References Cited by

For patients with partial jaw defects, cysts and dental implants, doctors need to take panoramic X-ray films or manually draw dental arch lines to generate Panorama images in order to observe their complete dentition information during oral diagnosis. In order to solve the problems of additional burden for patients to take panoramic X-ray films and time-consuming issue for doctors to manually segment dental arch lines, this paper proposes an automatic panorama reconstruction method based on cone beam computerized tomography (CBCT). The V-network (VNet) is used to pre-segment the teeth and the background to generate the corresponding binary image, and then the Bezier curve is used to define the best dental arch curve to generate the oral panorama. In addition, this research also addressed the issues of mistakenly recognizing the teeth and jaws as dental arches, incomplete coverage of the dental arch area by the generated dental arch lines, and low robustness, providing intelligent methods for dental diagnosis and improve the work efficiency of doctors.

Citation： HOU Changpeng, ZHU Fudong, ZHANG Gaohua, LYU Zhen, LIU Yunfeng, ZHU Weidong. Oral panorama reconstruction method based on pre-segmentation and Bezier function. Journal of Biomedical Engineering, 2023, 40(5): 894-902. doi: 10.7507/1001-5515.202302036 Copy

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7.	Bui T, Hamamoto K, Paing M P. Deep fusion feature extraction for caries detection on dental panoramic radiographs. Applied Sciences, 2021, 11(5): 2005.
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15.	Hojjatoleslami S A, Kruggel F. Segmentation of large brain lesions. IEEE Transactions on Medical Imaging, 2001, 20(7): 666-669.
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17.	田素坤, 戴宁, 袁福来, 等. 多级层次三维卷积神经网络的牙颌模型分割与识别技术. 计算机辅助设计与图形学学报, 2020, 32(8): 1218-1227.
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20.	Cui W, Zeng L, Chong B, et al. Toothpix: pixel-level tooth segmentation in panoramic X-ray images based on generative adversarial networks//2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), IEEE, 2021.
21.	Bae M, Park J W, Kim N. Semi-automatic and robust determination of dental arch form in dental cone-beam CT with B-spline approximation. Computer Methods and Programs in Biomedicine, 2019, 172: 95-101.
22.	Ammad M, Ramli A. Cubic B-spline curve interpolation with arbitrary derivatives on its data points//2019 23rd International Conference in Information Visualization–Part II, IEEE, 2019: 156-159.
23.	Yun Z, Yang S, Huang E, et al. Automatic reconstruction method for high-contrast panoramic image from dental cone-beam CT data. Computer Methods and Programs in Biomedicine, 2019, 175: 205-214.
24.	Guo M, Li Y, Su Y, et al. Rapid image deconvolution and multiview fusion for optical microscopy. Nature Biotechnology, 2020, 38(11): 1337-1346.
25.	Amorim P H J, Moraes T F, Silva J V L, et al. Reconstruction of panoramic dental images through Bézier function optimization. Frontiers in Bioengineering and Biotechnology, 2020, 8: 794.
26.	Milletari F, Navab N, Ahmadi S. V-net: fully convolutional neural networks for volumetric medical image segmentation//2016 4th International Conference on 3D Vision (3DV). IEEE, 2016: 565-571.
27.	Zhang T Y, Suen C Y. A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 1984, 27(3): 236-239.
28.	Powell M J D. Nonlinear programming-sequential unconstrained minimization techniques. The Computer Journal, 1969, 12(3): 207.

1. Jin L J, Lamster I B, Greenspan J S, et al. Global burden of oral diseases: emerging concepts, management and interplay with systemic health. Oral Diseases, 2016, 22(7): 609-619.
2. Yazdanian M, Karami S, Tahmasebi E, et al. Dental radiographic/digital radiography technology along with biological agents in human identification. Scanning, 2022, 2022: 5265912.
3. Cortes A R. Digital dentistry: a step-by-step guide and case atlas. John Wiley & Sons, 2022.
4. Price J B. Digital imaging. Clinical Applications of Digital Dental Technology, 2022. DOI: 10.1002/9781119800613.ch1.
5. 苏少华, 任少华. CBCT在口腔颌面外科诊疗中的应用及进展. 疾病监测与控制, 2020, 14(6): 501-504.
6. Araujo G T T, Peralta-Mamani M, Silva A F M D, et al. Influence of cone beam computed tomography versus panoramic radiography on the surgical technique of third molar removal: a systematic review. International Journal of Oral and Maxillofacial Surgery, 2019, 48(10): 1340-1347.
7. Bui T, Hamamoto K, Paing M P. Deep fusion feature extraction for caries detection on dental panoramic radiographs. Applied Sciences, 2021, 11(5): 2005.
8. Lurie A, Tosoni G M, Tsimikas J, et al. Recursive hierarchic segmentation analysis of bone mineral density changes on digital panoramic images. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 2012, 113(4): 549-558.
9. Tikhe S V, Naik A M, Bhide S D, et al. Algorithm to identify enamel caries and interproximal caries using dental digital radiographs//2016 IEEE 6th International Conference on Advanced Computing (IACC), IEEE, 2016.
10. Tuan T M. A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation. Expert Systems with Applications, 2016, 46: 380-393.
11. Trivedi D N, Kothari A M, Shah S, et al. Dental image matching by Canny algorithm for human identification. International Journal of Advanced Computer Research, 2014, 4(4): 985.
12. Mohanapriya N, Kalaavathi B. Adaptive image enhancement using hybrid particle swarm optimization and watershed segmentation. Intelligent Automation & Soft Computing, 2019, 25(4): 663-672.
13. Alsmadi M K. A hybrid fuzzy C-means and neutrosophic for jaw lesions segmentation. Ain Shams Engineering Journal, 2018, 9(4): 697-706.
14. Li H, Sun G, Sun H, et al. Watershed algorithm based on morphology for dental X-ray images segmentation//2012 IEEE 11th International conference on signal processing. IEEE, 2012: 877-880.
15. Hojjatoleslami S A, Kruggel F. Segmentation of large brain lesions. IEEE Transactions on Medical Imaging, 2001, 20(7): 666-669.
16. Habib A B, Akhter M E, Sultaan R, et al. Performance analysis of different 2D and 3D CNN model for liver semantic segmentation: a review// Proceeding of 2020 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2020). Singapore: Springer, 2020: 166-174.
17. 田素坤, 戴宁, 袁福来, 等. 多级层次三维卷积神经网络的牙颌模型分割与识别技术. 计算机辅助设计与图形学学报, 2020, 32(8): 1218-1227.
18. Koch T L, Perslev M, Igel C, et al. Accurate segmentation of dental panoramic radiographs with U-Nets//2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, 2019: 15-19.
19. Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation//Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016), Athens: Springer, 2016: 424-432.
20. Cui W, Zeng L, Chong B, et al. Toothpix: pixel-level tooth segmentation in panoramic X-ray images based on generative adversarial networks//2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), IEEE, 2021.
21. Bae M, Park J W, Kim N. Semi-automatic and robust determination of dental arch form in dental cone-beam CT with B-spline approximation. Computer Methods and Programs in Biomedicine, 2019, 172: 95-101.
22. Ammad M, Ramli A. Cubic B-spline curve interpolation with arbitrary derivatives on its data points//2019 23rd International Conference in Information Visualization–Part II, IEEE, 2019: 156-159.
23. Yun Z, Yang S, Huang E, et al. Automatic reconstruction method for high-contrast panoramic image from dental cone-beam CT data. Computer Methods and Programs in Biomedicine, 2019, 175: 205-214.
24. Guo M, Li Y, Su Y, et al. Rapid image deconvolution and multiview fusion for optical microscopy. Nature Biotechnology, 2020, 38(11): 1337-1346.
25. Amorim P H J, Moraes T F, Silva J V L, et al. Reconstruction of panoramic dental images through Bézier function optimization. Frontiers in Bioengineering and Biotechnology, 2020, 8: 794.
26. Milletari F, Navab N, Ahmadi S. V-net: fully convolutional neural networks for volumetric medical image segmentation//2016 4th International Conference on 3D Vision (3DV). IEEE, 2016: 565-571.
27. Zhang T Y, Suen C Y. A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 1984, 27(3): 236-239.
28. Powell M J D. Nonlinear programming-sequential unconstrained minimization techniques. The Computer Journal, 1969, 12(3): 207.

Journal of Biomedical Engineering

Oral panorama reconstruction method based on pre-segmentation and Bezier function

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