G. M. Aldridge, J. T. Ratnanather, M. E. Martone, M. Terada, M. F. Beg et al., Semiautomated Shape Analysis of Dendrite Spines from Animal Models of Fragilex and Parkinson's Disease using Large Deformation Diffeomorphic Metric Mapping, Society for Neuroscience Annual Meeting, 2005.

S. Allassonnì-ere, Y. Amit, and A. Trouvé, Towards a coherent statistical framework for dense deformable template estimation, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.69, pp.3-29, 2007.

S. Allassonnì-ere and E. Kuhn, Convergent stochastic expectation maximization algorithm with efficient sampling in high dimension. Application to deformable template model estimation, Comput. Statist. Data Anal, pp.91-95, 2015.

S. Allassonnì-ere and E. Kuhn, Stochastic algorithm for Bayesian mixture effect template estimation, ESAIM: Probability and Statistics, vol.14, pp.382-408, 2010.
DOI : 10.1051/ps/2009001

S. Allassonnì-ere, E. Kuhn, and A. Trouvé, Bayesian consistent estimation in deformable models using stochastic algorithms: Applications to medical images, J. Soc. Fran. Statist, vol.151, pp.1-16, 2010.

S. Allassonnì-ere, E. Kuhn, and A. Trouvé, Construction of Bayesian deformable models via a stochastic approximation algorithm: A convergence study, Bernoulli, pp.16-641, 2010.

S. Allassonnire, A. Trouvé, and L. Younes, Geodesic shooting and diffeomorphic matching via textured meshes, in Energy Minimization Methods for Computer Vision and Pattern Recognition, pp.365-381, 2005.

C. Andrieu, ´. E. Moulines, and P. Priouret, Stability of Stochastic Approximation under Verifiable Conditions, SIAM Journal on Control and Optimization, vol.44, issue.1, pp.283-312, 2005.
DOI : 10.1137/S0363012902417267

URL : https://hal.archives-ouvertes.fr/hal-00023475

. Aronszajn, Theory of reproducing kernels, Transactions of the American Mathematical Society, vol.68, issue.3, pp.337-404, 1950.
DOI : 10.1090/S0002-9947-1950-0051437-7

F. R. Bach, Consistency of the group lasso and multiple kernel learning, J. Mach. Learn. Res, vol.9, pp.1179-1225, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00164735

A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, pp.183-202, 2009.
DOI : 10.1137/080716542

M. F. Beg, M. I. Miller, A. Trouvé, and L. Younes, Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms, International Journal of Computer Vision, vol.61, issue.2, pp.61-139, 2005.
DOI : 10.1023/B:VISI.0000043755.93987.aa

J. Bigot, S. Gadat, and J. Loubes, Statistical M-Estimation and Consistency in Large Deformable Models for Image Warping, Journal of Mathematical Imaging and Vision, vol.23, issue.2, pp.270-290, 2009.
DOI : 10.1007/s10851-009-0146-1

E. Ceyhan, L. Fong, T. N. Tasky, M. K. Hurdal, M. F. Beg et al., Type-Specific Analysis of Morphometry of Dendrite Spines of Mice, 2007 5th International Symposium on Image and Signal Processing and Analysis, pp.7-12, 2007.
DOI : 10.1109/ISPA.2007.4383655

E. Ceyhan, R. Lken, L. Fong, T. N. Tasky, M. K. Hurdal et al., Modeling metric distances of dendrite spines of mice based on morphometric measures, International Symposium on Health Informatics and Bioinformatics, 2007.

G. E. Christensen, R. D. Rabbitt, and M. I. Miller, Deformable templates using large deformation kinematics, IEEE Transactions on Image Processing, vol.5, issue.10, pp.1435-1447, 1996.
DOI : 10.1109/83.536892

B. Delyon, M. Lavielle, and . Moulines, Convergence of a stochastic approximation version of the EM algorithm, Ann. Statist, vol.27, pp.94-128, 1999.

P. Dupuis, U. Grenander, and M. I. Miller, Variational problems on flows of diffeomorphisms for image matching, Quarterly of Applied Mathematics, vol.56, issue.3, pp.587-600, 1998.
DOI : 10.1090/qam/1632326

S. Durrleman, Statistical Models of Currents for Measuring the Variability of Anatomical Curves, Surfaces and their Evolution, 2010.
URL : https://hal.archives-ouvertes.fr/tel-00631382

S. Durrleman, S. Allassonnì, and S. Joshi, Sparse Adaptive Parameterization of Variability in Image Ensembles, International Journal of Computer Vision, vol.67, issue.2, pp.161-183, 2013.
DOI : 10.1007/s11263-012-0556-1

URL : https://hal.archives-ouvertes.fr/hal-00817565

S. Durrleman, M. Prastawa, G. Gerig, and S. Joshi, Optimal Data-Driven Sparse Parameterization of Diffeomorphisms for Population Analysis, Information Processing in Medical Imaging, pp.123-134, 2011.
DOI : 10.1007/978-3-642-22092-0_11

URL : https://hal.archives-ouvertes.fr/hal-00818405

J. Glaunès, M. Vaillant, and M. I. Miller, Landmark Matching via Large Deformation Diffeomorphisms on the Sphere, Journal of Mathematical Imaging and Vision, vol.20, issue.1/2, pp.179-200, 2004.
DOI : 10.1023/B:JMIV.0000011326.88682.e5

U. Grenander, General Pattern Theory, 1993.

U. Grenander and M. I. Miller, Computational anatomy: an emerging discipline, Quarterly of Applied Mathematics, vol.56, issue.4, pp.617-694, 1998.
DOI : 10.1090/qam/1668732

D. R. Holm, J. T. Ratnanather, A. Trouvé, and L. Younes, Soliton dynamics in computational anatomy, NeuroImage, vol.23, pp.170-178, 2004.
DOI : 10.1016/j.neuroimage.2004.07.017

S. Joshi and M. I. Miller, Landmark matching via large deformation diffeomorphisms, IEEE Transactions on Image Processing, vol.9, issue.8, pp.1357-1370, 2000.
DOI : 10.1109/83.855431

E. Kuhn and M. Lavielle, Coupling a stochastic approximation version of EM with an MCMC procedure, ESAIM: Probability and Statistics, vol.8, pp.115-131, 2004.
DOI : 10.1051/ps:2004007

J. Ma, M. I. Miller, A. Trouvé, and L. Younes, Bayesian template estimation in computational anatomy, NeuroImage, vol.42, issue.1, pp.42-252, 2008.
DOI : 10.1016/j.neuroimage.2008.03.056

S. Marsland and C. Twining, Constructing Diffeomorphic Representations for the Groupwise Analysis of Nonrigid Registrations of Medical Images, IEEE Transactions on Medical Imaging, vol.23, issue.8, pp.1006-1020, 2004.
DOI : 10.1109/TMI.2004.831228

M. Micheli, P. W. Michor, and D. Mumford, Sectional Curvature in Terms of the Cometric, with Applications to the Riemannian Manifolds of Landmarks, SIAM Journal on Imaging Sciences, vol.5, issue.1, pp.394-433, 2012.
DOI : 10.1137/10081678X

M. Miller, C. E. Priebe, A. Qiu, B. Fischl, A. Kolasny et al., Collaborative computational anatomy: An MRI morphometry study of the human brain via diffeomorphic metric mapping, Human Brain Mapping, vol.25, issue.7, pp.30-2132, 2009.
DOI : 10.1002/hbm.20655

M. I. Miller, A. Trouvé, and L. Younes, On the Metrics and Euler-Lagrange Equations of Computational Anatomy, Annual Review of Biomedical Engineering, vol.4, issue.1, pp.375-405, 2002.
DOI : 10.1146/annurev.bioeng.4.092101.125733

M. I. Miller, A. Trouvé, and L. Younes, Geodesic Shooting for Computational Anatomy, Journal of Mathematical Imaging and Vision, vol.13, issue.1???2, pp.209-228, 2006.
DOI : 10.1007/s10851-005-3624-0

M. I. Miller and L. Younes, Group action, diffeomorphism and matching: A general framework, Int. J. Comput. Vision, pp.41-61, 2001.

X. Pennec, Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements, Journal of Mathematical Imaging and Vision, vol.20, issue.10, pp.127-154, 2006.
DOI : 10.1007/s10851-006-6228-4

URL : https://hal.archives-ouvertes.fr/inria-00614994

L. Risser, F. Vialard, R. Wolz, M. Murgasova, D. D. Holm et al., Simultaneous Multi-scale Registration Using Large Deformation Diffeomorphic Metric Mapping, IEEE Transactions on Medical Imaging, vol.30, issue.10, pp.30-1746, 2011.
DOI : 10.1109/TMI.2011.2146787

S. Sommer, F. Lauze, M. Nielsen, and X. Pennec, Sparse Multi-Scale Diffeomorphic Registration: The Kernel Bundle Framework, Journal of Mathematical Imaging and Vision, vol.45, issue.1, Suppl.??1, pp.46-292, 2013.
DOI : 10.1007/s10851-012-0409-0

URL : https://hal.archives-ouvertes.fr/hal-00813868

J. Thirion, Image matching as a diffusion process: an analogy with Maxwell's demons, Medical Image Analysis, vol.2, issue.3, pp.243-260, 1998.
DOI : 10.1016/S1361-8415(98)80022-4

A. Trouvé, Diffeomorphism groups and pattern matching in image analysis, International Journal of Computer Vision, vol.28, issue.3, pp.213-221, 1998.
DOI : 10.1023/A:1008001603737

M. Vaillant, M. I. Miller, A. Trouvé, and L. Younes, Statistics on diffeomorphisms via tangent space representations, NeuroImage, vol.23, pp.161-169, 2004.
DOI : 10.1016/j.neuroimage.2004.07.023

F. Vialard and L. Risser, Spatially-Varying Metric Learning for Diffeomorphic Image Registration: A Variational Framework, Medical Image Computing and Computer-Assisted Intervention -MICCAI 2014, pp.227-234, 2014.
DOI : 10.1007/978-3-319-10404-1_29

M. Zhang, N. Singh, and P. T. Fletcher, Bayesian Estimation of Regularization and Atlas Building in Diffeomorphic Image Registration, Lecture Notes in Comput. Sci, vol.7917, pp.37-48, 2013.
DOI : 10.1007/978-3-642-38868-2_4