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Estimating patient-specific and anatomically correct reference model for
craniomaxillofacial deformity via sparse representation
1.J. J. Xia, J. Gateno, and J. F. Teichgraeber, “New clinical protocol to evaluate craniomaxillofacial deformity and plan surgical correction,” J. Oral Maxillofac. Surg. 67, 2093–2106 (2009).
2.T. A. Lew, J. A. Walker, J. C. Wenke, L. H. Blackbourne, and R. G. Hale, “Characterization of craniomaxillofacial battle injuries sustained by United States service members in the current conflicts of Iraq and Afghanistan,” J. Oral Maxillofac. Surg. 68, 3–7 (2010).
3.C. M. Gorlin RJ and R. C. M. Hennekam, Syndromes of the Head and Neck (Oxford University Press, New York, NY, 2001).
4.J. Gateno, J. J. Xia, J. F. Teichgraeber, A. M. Christensen, J. J. Lemoine, M. A. K. Liebschner, M. J. Gliddon, and M. E. Briggs, “Clinical feasibility of computer-aided surgical simulation (CASS) in the treatment of complex cranio-maxillofacial deformities,” J. Oral Maxillofac. Surg. 65, 728–734 (2007).
6.G. R. J. Swennen, E. L. Barth, C. Eulzer, and F. Schutyser, “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. 36, 146–152 (2007).
7.G. R. J. Swennen, M. Y. Mommaerts, J. Abeloos, C. De Clercq, P. Lamoral, N. Neyt, J. Casselman, and F. Schutyser, “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. 18, 533–539 (2007).
8.M. J. Troulis, P. Everett, E. B. Seldin, R. Kikinis, and L. B. Kaban, “Development of a three-dimensional treatment planning system based on computed tomographic data,” Int. J. Oral Maxillofac. Surg. 31, 349–357 (2002).
9.J. Xia, H. H. S. Ip, N. Samman, D. Wang, C. S. B. Kot, R. W. K. Yeung, and H. Tideman, “Computer-assisted three-dimensional surgical planning and simulation: 3D virtual osteotomy,” Int. J. Oral Maxillofac. Surg. 29, 11–17 (2000).
10.J. Helfrick, “Modern practice in orthognathic and reconstructive surgery. Edited by William H. Bell. WB Saunders Co, Philadelphia, Pennsylvania, vols. 1, 2, 3, 4, 1992, 2517 pp, $150.00 each,” Head & Neck 15, 587–588 (1993).
12.J. J. Xia, J. Gateno, and J. F. Teichgraeber, “Three-dimensional computer-aided surgical simulation for maxillofacial surgery,” Atlas Oral Maxillofac. Surg. Clin. 13, 25–39 (2005).
13.S. Zachow, H. Lamecker, B. Elsholtz, and M. Stiller, “Reconstruction of mandibular dysplasia using a statistical 3D shape model,” Int. Congr. Ser. 1281, 1238–1243 (2005).
14.W. Zhang, P. Yan, and X. Li, “Estimating patient-specific shape prior for medical image segmentation,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011 (IEEE, Chicago, IL, 2011), pp. 1451–1454.
15.Y. Zhu, X. Papademetris, A. J. Sinusas, and J. S. Duncan, “Segmentation of the left ventricle from cardiac MR images using a subject-specific dynamical model,” IEEE Trans. Med. Imaging 29, 669–687 (2010).
16.S. Zhang, Y. Zhan, M. Dewan, J. Huang, D. N. Metaxas, and X. S. Zhou, “Towards robust and effective shape modeling: Sparse shape composition,” Med. Image Anal. 16, 265–277 (2012).
17.G. Wang, S. Zhang, F. Li, and L. Gu, “A new segmentation framework based on sparse shape composition in liver surgery planning system,” Med. Phys. 40, 051913 (11pp.) (2013).
18.D. L. Donoho, “For most large underdetermined systems of linear equations the minimal,” Commun. Pure Appl. Math. 59, 797–829 (2006).
20.Y. Ren, L. Wang, Y. Gao, Z. Tang, K. C. Chen, J. Li, S. G. F. Shen, J. Yan, P. K. M. Lee, B. Chow, J. J. Xia, and D. Shen, “Estimating anatomically-correct reference model for craniomaxillofacial deformity via sparse representation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, edited by P. Golland, N. Hata, C. Barillot, J. Hornegger, and R. Howe (Springer, Boston, MA, 2014), Vol. 8674, pp. 73–80.
21.G. R. Swennen, F. A. Schutyser, and J. E. Hausamen, Three-dimensional Cephalometry: A Color Atlas and Manual (Springer, New York, NY, 2005).
22.J. J. Xia, J. K. McGrory, J. Gateno, J. F. Teichgraeber, B. C. Dawson, K. A. Kennedy, R. E. Lasky, J. D. English, C. H. Kau, and K. R. McGrory, “A new method to Orient 3-Dimensional computed tomography models to the natural head position: A clinical feasibility study,” J. Oral Maxillofac. Surg. 69, 584–591 (2011).
23.D. L. Donoho and M. Elad, “Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization,” Proc. Natl. Acad. Sci. U. S. A. 100, 2197–2202 (2003).
24.J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
25.J. L. Starck, M. Elad, and D. L. Donoho, “Image decomposition via the combination of sparse representations and a variational approach,” IEEE Trans. Image Process. 14, 1570–1582 (2005).
26.R. J. Tibshirani, “Regression shrinkage and selection via the lasso,” J. R. Stat. Soc., Ser. B 58, 267–288 (1996).
27.F. L. Bookstein, “Principal warps: Thin-plate splines and the decomposition of deformations,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 567–585 (1989).
30.Z. Xue, D. Shen, and C. Davatzikos, “Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warping,” Med. Image Anal. 10, 740–751 (2006).
31.J. Yan, S. G. F. Shen, B. Fang, H. Shi, Y. Wu, Z. Shao, B. Xia, and D. Yu, “Three-dimensional CT measurements for the craniomaxillofacial structure of normal occlusion adult in Jiangsu Zhejiang and Shanghai areas,” China J. Oral Maxillofac. Surg. 8, 2–9 (2010).
36.L. Wang, F. Shi, Y. Gao, G. Li, J. H. Gilmore, W. Lin, and D. Shen, “Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation,” NeuroImage 89, 152–164 (2014).
37.L. Wang, K. C. Chen, Y. Gao, F. Shi, S. Liao, G. Li, S. G. F. Shen, J. Yan, P. K. M. Lee, B. Chow, N. X. Liu, J. J. Xia, and D. Shen, “Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization,” Med. Phys. 41, 043503(14pp.) (2014).
41.T. L. Jones, “Fluctuation Asymmetry of the Facial Skeleton in a Normal Chinese Population,” Master thesis, School of Dentistry at Houston, The University of Texas, 2013.
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A significant number of patients suffer from craniomaxillofacial (CMF) deformity
and require CMF surgery in the United States. The success of CMF surgery depends
on not only the surgical techniques but also an accurate surgical planning.
However, surgical planning for CMF surgery is challenging due to the absence of a
patient-specific reference model. Currently, the outcome of the
surgery is often subjective and highly dependent on surgeon’s experience. In this
paper, the authors present an automatic method to estimate an anatomically correct
reference shape of jaws for orthognathic surgery, a common type of CMF
To estimate a patient-specific jaw reference model, the
authors use a data-driven method based on sparse shape composition. Given a
dictionary of normal subjects, the authors first use the
sparse representation to represent the midface of a patient by the midfaces of the
normal subjects in the dictionary. Then, the derived sparse coefficients are used to
reconstruct a patient-specific reference jaw shape.
The authors have validated the proposed method on both synthetic and real patient
data. Experimental results show that the authors’ method can effectively
reconstruct the normal shape of jaw for patients.
The authors have presented a novel method to automatically estimate a
patient-specific reference model for the patient suffering from CMF
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