Abstract
The morphology of the craniofacial structure often varies in the general population, but the severities of the variation from the normal physiological structure can reveal potential disorders that affect the patient’s quality of life. In recent years, the preferred method for diagnosis and treatment of patients with craniofacial disorders has been using Cone Beam Computed Tomography (CBCT) imaging accompanied by manual segmentation to produce a 3D model of the craniofacial region. Unfortunately, manual segmentation is often tedious, user-dependent, and costly. The field of machine learning has flourished in recent years due to improvements in computer processing power, as well as storage capacity. Machine learning in different areas of the medical field has also been up-and-coming. One beneficial application of machine learning is automatic volumetric segmentation. This learning based method is much faster and can be even more accurate than manual segmentation. This chapter will first introduce the process of automated segmentation in detail, from preparing annotated data to developing a neural network. Then we will look at an application on a specific craniofacial variation, patients with unilaterally impacted canines. The review illustrates the feasibility and benefit of using machine learning in orthodontic treatment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kim DW, Kim H, Nam W, Kim HJ, Cha I-H. Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: a preliminary report. Bone. 2018;116:207–14. https://doi.org/10.1016/j.bone.2018.04.020.
Montenegro RD, Oliveira ALI, Cabral GG, Katz CRT, Rosenblatt A. A comparative study of machine learning techniques for caries prediction. In: 2008 20th IEEE international conference on tools with artificial intelligence, 2008, vol. 2, pp. 477–481, https://doi.org/10.1109/ICTAI.2008.138.
Hammond P, Hutton T, Maheswaran S, Modgil S. Computational models of oral and craniofacial development, growth, and repair. Adv Dent Res. 2003;17(1):61–4. https://doi.org/10.1177/154407370301700114.
Murata S, Lee C, Tanikawa C, Date S. Towards a fully automated diagnostic system for orthodontic treatment in dentistry. In: 2017 IEEE 13th international conference on e-science (e-science), 2017, pp. 1–8, https://doi.org/10.1109/eScience.2017.12.
Gan Y, Xia Z, Xiong J, Zhao Q, Hu Y, Zhang J. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model. Med Phys. 2015;42(1):14–27. https://doi.org/10.1118/1.4901521.
Wang L, et al. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization. Med Phys. 2014;41(4):043503. https://doi.org/10.1118/1.4868455.
Adams R, Bischof L. Seeded region growing. IEEE Trans Pattern Anal Mach Intell. 1994;16(6):641–7. https://doi.org/10.1109/34.295913.
Chen GC, Sun M, Yin NB, Li HD. A novel method to calculate the volume of alveolar cleft defect before surgery. J Craniofac Surg., vol. 29, no. 2, 2018 [Online]. Available from https://journals.lww.com/jcraniofacialsurgery/Fulltext/2018/03000/A_Novel_ Method_to_Calculate_the_Volume_of_Alveolar.20. aspx
Yushkevich PA, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage. 2006;31(3):1116–28. https://doi.org/10.1016/j.neuroimage.2006.01.015.
Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation BT – Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 2015, pp. 234–241.
Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse annotation BT – Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, 2016, pp. 424–432.
Ciresan D, Giusti A, Gambardella LM, Schmidhuber J. Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in neural information processing systems, 2012, pp. 2843–2851.
Sudre CH, Li W, Vercauteren T, Ourselin S, Jorge Cardoso M. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations BT – Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 2017, pp. 240–248.
Reddi SJ, Kale S, Kumar S. On the convergence of Adam and beyond, eprint arXiv:1904.09237. p. arXiv:1904.09237, 01-Apr-2019 [Online]. Available from https://ui.adsabs.harvard.edu/abs/2019arXiv190409237R
Chen S, et al. Machine learning in orthodontics: Introducing a 3D auto-segmentation and auto-landmark finder of CBCT images to assess maxillary constriction in unilateral impacted canine patients. Angle Orthod. 2019;90(1):77–84. https://doi.org/10.2319/012919-59.1.
O’Neill J. Maxillary expansion as an interceptive treatment for impacted canines. Evid Based Dent. vol. 11, p. 86, Sep. 2010 [Online]. https://doi.org/10.1038/sj.ebd.6400742.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Chen, S. et al. (2021). Machine (Deep) Learning for Characterization of Craniofacial Variations. In: Ko, CC., Shen, D., Wang, L. (eds) Machine Learning in Dentistry. Springer, Cham. https://doi.org/10.1007/978-3-030-71881-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-71881-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71880-0
Online ISBN: 978-3-030-71881-7
eBook Packages: MedicineMedicine (R0)