Statistical shape analysis of large datasets using diffeomorphic iterative centroids

Abstract : Statistical shape analysis methods are increasingly used in neuroscience and clinical research. A current challenge for methodological research is to perform statistical analysis on large datasets (several hundreds or thousands of subjects). A common approach in morphometry is template-based analysis where one analyzes the deformations that map individuals to a template of the population (Ashburner 1998; Vaillant 2004). The Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework provides a natural setting for quantifying deformations between anatomical shapes. Various methods have been proposed to estimate a template using the LDDMM framework (Durrleman 2008; Glaunès 2006). However, their application to large datasets has remained limited due to their high computational load. We present a fast method for template-based shape analysis in the LDDMM framework. We evaluate the approach on synthetic and real datasets of hippocampal shapes, including a large dataset of 1000 subjects.
Type de document :
Communication dans un congrès
Human Brain Mapping - 2015, Jun 2015, Honolulu, United States
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https://hal.inria.fr/hal-01212226
Contributeur : Claire Cury <>
Soumis le : mardi 6 octobre 2015 - 12:27:54
Dernière modification le : dimanche 26 novembre 2017 - 01:10:55

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  • HAL Id : hal-01212226, version 1

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Claire Cury, Joan Glaunés, Roberto Toro, Gunter Shumann, Vincent Frouin, et al.. Statistical shape analysis of large datasets using diffeomorphic iterative centroids. Human Brain Mapping - 2015, Jun 2015, Honolulu, United States. 〈hal-01212226〉

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