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WP2 - Random shapes

In this work package we study random shapes that reside in non-linear spaces. An important example is the tree-spaces, arising naturally in modelling of anatomical networks. The field of non-linear statistics has close connections to functional data analysis, and random topology and graphs.

While there is a deep understanding of the mathematical and computational aspects of many data types living in non-linear spaces, a detailed understanding of random variation in non-linear spaces and how to handle randomness statistically is largely missing.

WP2 - Subprojects

WP2.1: Deformation modelling and statistics of deformations
WP2.2: Modelling and inference in non-linear spaces
WP2.3: Applications in diffusion weighted imaging

Research questions

In the second funding period of CSGB, we focus on projects in (a) deformation modelling and statistics of deformations and (b) modelling and inference in non-linear spaces.

  • Based on the large deformation diffeomorphic metric mapping (LDDMM) approach to deformation modelling, we aim at developing a stochastic framework for deformation groups. In a Bayesian analysis of the deformation models, we want to use prior distributions that arise from transition densities of stochastic differential equations.
  • The general aim is to develop statistical tools for data in non-linear spaces such as manifolds, stratified spaces and general metric spaces. The study of tree-spaces, initiated in the first funding period of CSGB, is continued. As a new challenge, inspired by voxel-based analysis of diffusion-weighted imaging (DWI) data, we want to develop statistical inference for mixture models for multi-tensors.

In the second funding period, we aim at using the developed mixture models for representing local diffusion orientation distributions that take crossing fiber diffusions into account. Mixture models have appeared previously, but crucial issues such as matching and aligning of modes have not been addressed.

Selected references

Feragen, A., Lo, P., de Bruijne, M., Nielsen, M. and Lauze, F. (2013): Toward a theory of statistical tree-shape analysis. IEEE T. Pattern Anal. 35, 2008-2021.

Hauberg, S., Feragen, A., Enficiaud, R. and Black, M.J. (2016): Scalable robust principal component analysis using Grassmann averages. IEEE T. Pattern Anal. 38, 2298-2311.

Liptrot, M. and Lauze, F. (2014):  A model-free unsupervised method to cluster brain tissue directly from DWI volumes. Proceedings of the Annual Meeting of the International Society for Magnetic Resonance in Medicine, ISMRM.

Liptrot, M. and Lauze, F. (2016): Rotationally invariant clustering of diffusion MRI data using spherical harmonics. To appear in Proceedings of SPIE Medical Imaging 2016.

Sommer, S.H. (2015): Anisotropic distributions on manifolds: template estimation and most probable paths. In Proceedings from the conference Information Processing in Medical Imaging (IPMI), Sabhal Mor Ostaig, Isle of Skye, UK. Springer LNCS 9123, pp. 193-204.

Sommer, S.H., Nielsen, M., Darkner, S. and Pennec, X. (2013): Higher-order momentum distributions and locally affine LDDMM registration. SIAM Journal on Imaging Sciences 6, 341-367.

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Revised 19.05.2017