Abstract
A large number of people require surgical or orthodontic treatment to correct jaw deformities. The accuracy of surgical planning is essential to the success of craniomaxillofacial (CMF) surgery. An accurate surgical plan greatly relies on a patient-specific reference model. The current challenge is a lack of this reference model. As a result, the outcome of surgery is currently dependent on the surgeon’s diagnoses and experience. This chapter introduces a method to automatically estimate an anatomically correct reference shape of the jaws for the patient requiring orthognathic surgery. The method is based on sparse shape composition and is data-driven. It can effectively estimate the normal shape of the maxilla and mandible.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Please Note: Unless otherwise specified, in the following text, the term “jaws” represents both upper (or maxillary) and lower (or mandibular) jaws.
References
Lew TA, Walker JA, Wenke JC, Blackbourne LH, Hale RG. Characterization of craniomaxillofacial battle injuries sustained by United States service members in the current conflicts of Iraq and Afghanistan. J Oral Maxillofac Surg. 2010;68(1):3–7. https://doi.org/10.1016/j.joms.2009.06.006.
Xia JJ, Gateno J, Teichgraeber JF. New clinical protocol to evaluate craniomaxillofacial deformity and plan surgical correction. J Oral Maxillofac Surg. 2009;67(10):2093–106. https://doi.org/10.1016/j.joms.2009.04.057.
Gateno J, Xia JJ, Teichgraeber JF, Christensen AM, Lemoine JJ, Liebschner MA, Gliddon MJ, Briggs ME. Clinical feasibility of computer-aided surgical simulation (CASS) in the treatment of complex cranio-maxillofacial deformities. J Oral Maxillofac Surg. 2007;65(4):728–34. https://doi.org/10.1016/j.joms.2006.04.001.
Swennen GR, Barth EL, Eulzer C, Schutyser F. The use of a new 3D splint and double CT scan procedure to obtain an accurate anatomic virtual augmented model of the skull. Int J Oral Maxillofac Surg. 2007;36(2):146–52. https://doi.org/10.1016/j.ijom.2006.09.019.
Swennen GR, Mommaerts MY, Abeloos J, De Clercq C, Lamoral P, Neyt N, Casselman J, Schutyser F. The use of a wax bite wafer and a double computed tomography scan procedure to obtain a three-dimensional augmented virtual skull model. J Craniofac Surg. 2007;18(3):533–9. https://doi.org/10.1097/scs.0b013e31805343df.
Xia J, Ip HH, Samman N, Wang D, Kot CS, Yeung RW, Tideman H. Computer-assisted three-dimensional surgical planning and simulation: 3D virtual osteotomy. Int J Oral Maxillofac Surg. 2000;29(1):11–7.
Zhang S, Zhan Y, Dewan M, Huang J, Metaxas DN, Zhou XS. Towards robust and effective shape modeling: sparse shape composition. Med Image Anal. 2012;16(1):265–77. https://doi.org/10.1016/j.media.2011.08.004.
Wang G, Zhang S, Li F, Gu L. A new segmentation framework based on sparse shape composition in liver surgery planning system. Med Phys. 2013;40(5):051913. https://doi.org/10.1118/1.4802215.
Zhang S, Zhan Y, Metaxas DN. Deformable segmentation via sparse representation and dictionary learning. Med Image Anal. 2012;16(7):1385–96. https://doi.org/10.1016/j.media.2012.07.007.
Donoho D. For most large underdetermined systems of linear equations the minimal L1-norm solution is also the sparsest solution. Commun Pure Appl Math. 2006;59:797–829.
Vannier MW, Marsh JL, Warren JO. Three dimensional CT reconstruction images for craniofacial surgical planning and evaluation. Radiology. 1984;150(1):179–84. https://doi.org/10.1148/radiology.150.1.6689758.
Xia JJ, Gateno J, Teichgraeber JF. Three-dimensional computer-aided surgical simulation for maxillofacial surgery. Atlas Oral Maxillofac Surg Clin North Am. 2005;13(1):25–39. https://doi.org/10.1016/j.cxom.2004.10.004.
Zachow S, Lamecker H, Elsholtz B, Stiller M. Reconstruction of mandibular dysplasia using a statistical 3D shape model. Int Congr Ser. 2005;1281 https://doi.org/10.1016/j.ics.2005.03.339.
Zhu Y, Papademetris X, Sinusas AJ, Duncan JS. Segmentation of the left ventricle from cardiac MR images using a subject-specific dynamical model. IEEE Trans Med Imaging. 2010;29(3):669–87. https://doi.org/10.1109/TMI.2009.2031063.
Zhang W, Yan P, Li X. Estimating patient-specific shape prior for medical image segmentation. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 30 March–2 April 2011, 2011. pp 1451–1454. https://doi.org/10.1109/ISBI.2011.5872673
Xia JJ, McGrory JK, Gateno J, Teichgraeber JF, Dawson BC, Kennedy KA, Lasky RE, English JD, Kau CH, McGrory KR. A new method to orient 3-dimensional computed tomography models to the natural head position: a clinical feasibility study. J Oral Maxillofac Surg. 2011;69(3):584–91. https://doi.org/10.1016/j.joms.2010.10.034.
Starck JL, Elad M, Donoho DL. Image decomposition via the combination of sparse representations and a variational approach. IEEE Trans Image Process. 2005;14(10):1570–82. https://doi.org/10.1109/tip.2005.852206.
Tibshirani R. Regression Shrinkage and selection via the Lasso. J R Stat Soc Ser B (Methodol). 1996;58(1):267–88.
Lapeer RJ, Prager RW. 3D shape recovery of a newborn skull using thin-plate splines. Comput Med Imaging Graph. 2000;24(3):193–204. https://doi.org/10.1016/s0895-6111(00)00019-7.
Xue Z, Shen D, Davatzikos C. Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warping. Med Image Anal. 2006;10(5):740–51. https://doi.org/10.1016/j.media.2006.06.007.
Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y. Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell. 2009;31(2):210–27. https://doi.org/10.1109/TPAMI.2008.79.
Yan J, Shen G-f, Fang B, Shi H-m, Wu Y, Shao Z-y, Xia B-q, Yu D-d. Three-dimensional CT measurement for the craniomaxillofacial structure of normal occlusion adults in Jiangsu, Zhejiang and Shanghai Area. China J Oral Maxillof Surg. 2010.
Wang L, Chen KC, Gao Y, Shi F, Liao S, Li G, Shen SG, Yan J, Lee PK, Chow B, Liu NX, Xia JJ, Shen D. 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.
Mairal J, Bach F, Ponce J. Task-driven dictionary learning. IEEE Trans Pattern Anal Mach Intell. 2012;34(4):791–804. https://doi.org/10.1109/TPAMI.2011.156.
Gateno J, Jones TL, Shen SGF, Chen KC, Jajoo A, Kuang T, English JD, Nicol M, Teichgraeber JF, Xia JJ. Fluctuating asymmetry of the normal facial skeleton. Int J Oral Maxillofac Surg. 2018;47(4):534–40. https://doi.org/10.1016/j.ijom.2017.10.011.
Wu G, Jia H, Wang Q, Shen D. SharpMean: groupwise registration guided by sharp mean image and tree-based registration. NeuroImage. 2011;56(4):1968–81. https://doi.org/10.1016/j.neuroimage.2011.03.050.
Acknowledgments
This work was supported in part by National Institutes of Health/National Institute of Dental and Craniofacial Research grants DE022676, DE021863 and DE027251. Dr. Chen was sponsored by the Taiwan Ministry of Education, and Dr. Tang was sponsored by the China Scholarship Council while they were working at the Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, USA.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Deng, H.H. et al. (2021). Patient-Specific Reference Model for Planning Orthognathic Surgery. In: Ko, CC., Shen, D., Wang, L. (eds) Machine Learning in Dentistry. Springer, Cham. https://doi.org/10.1007/978-3-030-71881-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-71881-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71880-0
Online ISBN: 978-3-030-71881-7
eBook Packages: MedicineMedicine (R0)