Estimation of smooth growth trajectories with controlled acceleration from time series shape data.

Abstract : Longitudinal shape analysis often relies on the estimation of a realistic continuous growth scenario from data sparsely distributed in time. In this paper, we propose a new type of growth model parameterized by acceleration, whereas standard methods typically control the velocity. This mimics the behavior of biological tissue as a mechanical system driven by external forces. The growth trajectories are estimated as smooth flows of deformations, which are twice differentiable. This differs from piecewise geodesic regression, for which the velocity may be discontinuous. We evaluate our approach on a set of anatomical structures of the same subject, scanned 16 times between 4 and 8 years of age. We show our acceleration based method estimates smooth growth, demonstrating improved regularity compared to piecewise geodesic regression. Leave-several-out experiments show that our method is robust to missing observations, as well as being less sensitive to noise, and is therefore more likely to capture the underlying biological growth.
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Communication dans un congrès
Gabor Fichtinger and Anne Martel and Terry Peters. MICCAI - Medical Image Computing And Computer Assisted Intervention, Sep 2011, Toronto, Canada. Springer, 6892 (Pt 2), pp.401-418, 2011, Lecture Notes in Computer Science (LNCS). 〈10.1007/978-3-642-23629-7_49〉
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https://hal.inria.fr/hal-00818406
Contributeur : Stanley Durrleman <>
Soumis le : vendredi 26 avril 2013 - 17:45:05
Dernière modification le : lundi 6 mai 2013 - 12:08:38

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James Fishbaugh, Stanley Durrleman, Guido Gerig. Estimation of smooth growth trajectories with controlled acceleration from time series shape data.. Gabor Fichtinger and Anne Martel and Terry Peters. MICCAI - Medical Image Computing And Computer Assisted Intervention, Sep 2011, Toronto, Canada. Springer, 6892 (Pt 2), pp.401-418, 2011, Lecture Notes in Computer Science (LNCS). 〈10.1007/978-3-642-23629-7_49〉. 〈hal-00818406〉

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