Abstract
This paper presents a generalized learning based framework for improving both speed and accuracy of the existing deformable registration method. The key of our framework involves the utilization of a support vector regression (SVR) to learn the correlation between brain image appearances and their corresponding shape deformations to a template, for helping significantly cut down the computation cost and improve the robustness to local minima by using the learned correlation to instantly predict a good subject-specific deformation initialization for any given subject under registration. Our framework consists of three major parts: 1) training of SVR models based on the statistics of image samples and their shape deformations to capture intrinsic image-deformation correlations, 2) deformation prediction for a new subject with the trained SVR models to generate a subject-resemblance intermediate template by warping the original template with the predicted deformations, and 3) estimating of the residual deformation from the intermediate template to the subject for refined registration. Any existing deformable registration methods can be easily employed for training the SVR models and estimating the residual deformation. We have tested in this paper the two widely used deformable registration algorithms, i.e., HAMMER [1] and diffeomorphic demons [2], for demonstration of our proposed frameowrk. Experimental results show that, compared to the registration using the original methods (with no deformation prediction), our framework achieves a significant speedup (6X faster than HAMMER, and 3X faster than diffeomorphic demons), while maintaining comparable (or even slighly better) registration accuracy.
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Keywords
- Support Vector Regression
- Deformation Field
- Registration Method
- Residual Deformation
- Support Vector Regression Model
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References
Shen, D., Davatzikos, C.: HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE Transactions on Medical Imaging 21, 1421–1439 (2002)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage 45, S61–S72 (2009)
Xue, Z., Shen, D., Davatzikos, C.: Statistical Representation of High-Dimensional Deformation Fields with Application to Statistically-Constrained 3D Warping. Medical Image Analysis 10, 740–751 (2006)
Rueckert, D., Frangi, A., Schnabel, J.: Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration. IEEE Transactions on Medical Imaging 22, 1014–1025 (2003)
Loeckx, D., Makes, F., Vandermeulen, D., Suetens, P.: Non-rigid image registration using a statistical spline deformation model. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 463–474. Springer, Heidelberg (2003)
Tang, S., Fan, Y., Wu, G., Kim, M., Shen, D.: RABBIT: rapid alignment of brains by building intermediate templates. Neuroimage 47, 1277–1287 (2009)
Cherkassky, V., Ma, Y.: Selection of meta-parameters for support vector regression. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 687–693. Springer, Heidelberg (2002)
Christensen, G.E., Geng, X., Kuhl, J.G.H., Bruss, J., Grabowski, T.J., Pirwani, I.A., Vannier, M.W., Allen, J.S., Damasio, H.: Introduction to the Non-rigid image registration evaluation project (NIREP). In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds.) WBIR 2006. LNCS, vol. 4057, pp. 128–135. Springer, Heidelberg (2006)
Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckerner, R.L.: Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults. Journal of Cognitive Neuroscience 19, 1498–1507 (2007)
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Kim, M., Wu, G., Yap, PT., Shen, D. (2010). A Generalized Learning Based Framework for Fast Brain Image Registration. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15745-5_38
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DOI: https://doi.org/10.1007/978-3-642-15745-5_38
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