Metodo

International Studies in Phenomenology and Philosophy

Series | Book | Chapter

224920

Diffeomorphic iterative centroid methods for template estimation on large datasets

Claire CuryOlivier Colliot

pp. 273-299

Abstract

A common approach for analysis of anatomical variability relies on the estimation of a template representative of the population. The Large Deformation Diffeomorphic Metric Mapping is an attractive framework for that purpose. However, template estimation using LDDMM is computationally expensive, which is a limitation for the study of large datasets. This chapter presents an iterative method which quickly provides a centroid of the population in the shape space. This centroid can be used as a rough template estimate or as initialization of a template estimation method. The approach is evaluated on datasets of real and synthetic hippocampi segmented from brain MRI. The results show that the centroid is correctly centered within the population and is stable for different orderings of subjects. When used as an initialization, the approach allows to substantially reduce the computation time of template estimation.

Publication details

Published in:

Nielsen Frank (2014) Geometric theory of information. Dordrecht, Springer.

Pages: 273-299

DOI: 10.1007/978-3-319-05317-2_10

Full citation:

Cury Claire, Colliot Olivier (2014) „Diffeomorphic iterative centroid methods for template estimation on large datasets“, In: F. Nielsen (ed.), Geometric theory of information, Dordrecht, Springer, 273–299.