Craniomaxillofacial Deformity Correction via Sparse Representation in Coherent Space
- 2.5k Downloads
Orthognathic surgery is popular for patients with craniomaxillofacial (CMF) deformity. For orthognathic surgical planning, it is critical to have a patient-specific jaw reference model as guidance. One way is to estimate a normal jaw shape for the patient, by first searching for a normal subject with similar midface and then borrowing his/her (normal) jaw shape as reference. Intuitively, we can search for multiple normal subjects with similar midface and then linearly combine them as final reference. The respective coefficients for linear combination can be estimated, i.e., by sparse representation of patient’s midface by midfaces of all training normal subjects. However, this approach implicitly assumes that the representation of midface shapes is strongly correlated with the representation of jaw shapes, which is unfortunately difficult to meet in practice due to generally different data distributions of shapes of midfaces and jaws. To address this limitation, we propose to estimate the patient-specific jaw reference model in a coherent space. Specifically, we first employ canonical correlation analysis (CCA) to map the midface and jaw landmarks of training normal subjects into a coherent space, in which their correlation is maximized. Then, in the coherent space, the mapped midface landmarks of patient can be sparsely represented by the mapped midface landmarks of training normal subjects. Those learned sparse coefficients can now be used to combine the jaw landmarks of training normal subjects for estimating the normal jaw landmarks for patient and then building normal jaw shape reference model. Moreover, we also iteratively maximize the correlation between the midface and the jaw shapes in the new coherent space with a multi-layer mapping and refinement (MMR) process. Experimental results on real clinical data show that the proposed method can more accurately reconstruct the normal jaw shape for patient than the competing methods.
Unable to display preview. Download preview PDF.
- 2.Ren, Y., Wang, L., Gao, Y., Tang, Z., Chen, K.C., Li, J., Shen, S.G., Yan, J., Lee, P.K., Chow, B., Xia, J.J., Shen, D.: Estimating anatomically-correct reference model for craniomaxillofacial deformity via sparse representation. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part II. LNCS, vol. 8674, pp. 73–80. Springer, Heidelberg (2014)Google Scholar
- 3.Swennen, G.R., Schutyser, F.A., Hausamen, J.E.: Three-dimensional cephalometry: a color atlas and manual. Springer (2005)Google Scholar
- 4.Wang, L., Chen, K.C., Gao, Y., Shi, F., Liao, S., Li, G., Shen, S.G.F., Yan, J., Lee, P.K.M., Chow, B., Liu, N.X., Xia, J.J., Shen, D.: Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization. Medical Physics 41(4), 043503 (2014)CrossRefGoogle Scholar
- 5.Xia, J.J., McGrory, J.K., Gateno, J., Teichgraeber, J.F., Dawson, B.C., Kennedy, K.A., Lasky, R.E., English, J.D., Kau, C.H., McGrory, K.R.: A new method to orient 3-dimensional com-puted tomography models to the natural head position: a clinical feasibility study. J. Oral Maxillofac. Surg. 69(3), 584–591 (2011)CrossRefGoogle Scholar
- 8.Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B, 267–288 (1996)Google Scholar
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.