Abstract
Template estimation plays a crucial role in computational anatomy since it provides reference frames for performing statistical analysis of the underlying anatomical population variability. While building models for template estimation, variability in sites and image acquisition protocols need to be accounted for. To account for such variability, we propose a generative template estimation model that makes simultaneous inference of both bias fields in individual images, deformations for image registration, and variance hyperparameters. In contrast, existing maximum a posterori based methods need to rely on either bias-invariant similarity measures or robust image normalization. Results on synthetic and real brain MRI images demonstrate the capability of the model to capture heterogeneity in intensities and provide a reliable template estimation from registration.
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Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. Neuroimage 23, S151–S160 (2004)
Zhang, M., Singh, N., Fletcher, P.T.: Bayesian estimation of regularization and atlas building in diffeomorphic image registration. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 37–48. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38868-2_4
Allassonniere, S., Kuhn, E.: Stochastic algorithm for parameter estimation for dense deformable template mixture model. ESAIM-PS 14, 382–408 (2010)
Raket, L., et al.: A nonlinear mixed-effects model for simultaneous smoothing and registration of functional data. Pattern Recogn. Lett. 38, 1–7 (2014)
Sommer, S., Lauze, F., Nielsen, M., Pennec, X.: Sparse multi-scale dieomorphic registration: the kernel bundle framework. JMIV 46(3), 292–308 (2012)
Pai, A., Sommer, S., Sorensen, L., Darkner, S., Sporring, J., Nielsen, M.: Kernel bundle diffeomorphic image registration using stationary velocity fields and Wendland basis functions. IEEE TMI PP(99) (2015)
Kochunov, P., Lancaster, J., Thompson, P., Woods, R., Mazziotta, J., Hardies, J., Fox, P.: Regional spatial normalization: toward an optimal target. J. Comput. Assist. Tomogr. 25(5), 805–816 (2001)
Rueckert, D., et al.: Automatic construction of 3D statistical deformation models of the brain using non-rigid registration. IEEE TMI 22(8), 1014–1025 (2003)
Vialard, F.X., Risser, L., Holm, D., Rueckert, D.: Diffeomorphic atlas estimation using Karcher mean and geodesic shooting on volumetric images. In: MIUA (2011)
Zöllei, L., Jenkinson, M., Timoner, S., Wells, W.: A marginalized MAP approach and EM optimization for pair-wise registration. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 662–674. Springer, Heidelberg (2007). doi:10.1007/978-3-540-73273-0_55
Hromatka, M., Zhang, M., Fleishman, G.M., Gutman, B., Jahanshad, N., Thompson, P., Fletcher, P.T.: A hierarchical Bayesian model for multi-site diffeomorphic image atlases. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 372–379. Springer, Cham (2015). doi:10.1007/978-3-319-24571-3_45
Henderson, C.R.: Estimation of genetic parameters. Biometrics 6, 186–187 (1950)
Si, S., et al.: Memory efficient kernel approximation. In: ICML (2014)
Robinson, G.: That BLUP is a good thing: the estimation of random effects. Stat. Sci. 6(1), 15–51 (1991)
Wendland, H.: Piecewise polynomial, positive definite and compactly supported radial functions of minimal degree. Adv. Comput. Math. 4(1), 389–396 (1995)
Sommer, S.: Anisotropic distributions on manifolds: template estimation and most probable paths. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 193–204. Springer, Cham (2015). doi:10.1007/978-3-319-19992-4_15
Sommer, S.: Anisotropic distributions on manifolds: template estimation and most probable paths. Information Processing in Medical Imaging 193–204(2015)
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Pai, A. et al. (2017). A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images. In: Müller, H., et al. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. BAMBI MCV 2016 2016. Lecture Notes in Computer Science(), vol 10081. Springer, Cham. https://doi.org/10.1007/978-3-319-61188-4_14
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DOI: https://doi.org/10.1007/978-3-319-61188-4_14
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