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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.
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Contributor : Claire Cury Connect in order to contact the contributor
Submitted on : Tuesday, October 6, 2015 - 12:27:54 PM
Last modification on : Thursday, September 1, 2022 - 4:04:02 AM


  • HAL Id : hal-01212226, version 1


Claire Cury, Joan Glaunés, Roberto Toro, Gunter D Schumann, 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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