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Optimal data-driven sparse parameterization of diffeomorphisms for population analysis.

Abstract : In this paper, we propose a novel approach for intensity based atlas construction from a population of anatomical images, that estimates not only a template representative image but also a common optimal parameterization of the anatomical variations evident in the population. First, we introduce a discrete parameterization of large diffeomorphic deformations based on a finite set of control points, so that deformations are characterized by a low dimensional geometric descriptor. Second, we optimally estimate the position of the control points in the template image domain. As a consequence, control points move to where they are needed most to capture the geometric variability evident in the population. Third, the optimal number of control points is estimated by using a log - L1 sparsity penalty. The estimation of the template image, the template-to-subject mappings and their optimal parameterization is done via a single gradient descent optimization, and at the same computational cost as independent template-to-subject registrations. We present results that show that the anatomical variability of the population can be encoded efficiently with these compact and adapted geometric descriptors.
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Contributor : Stanley Durrleman <>
Submitted on : Friday, April 26, 2013 - 5:42:41 PM
Last modification on : Monday, November 16, 2020 - 10:52:06 AM

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Stanley Durrleman, Marcel Prastawa, Guido Gerig, Sarang Joshi. Optimal data-driven sparse parameterization of diffeomorphisms for population analysis.. 22nd International Conference, IPMI 2011, Jul 2011, Kloster Irsee, Germany. pp.123-34, ⟨10.1007/978-3-642-22092-0_11⟩. ⟨hal-00818405⟩



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