Inria Paris-Rocquencourt, UPMC - Université Pierre et Marie Curie - Paris 6, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
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.
https://hal.inria.fr/hal-01212226
Contributeur : Claire Cury <>
Soumis le : mardi 6 octobre 2015 - 12:27:54 Dernière modification le : mercredi 14 avril 2021 - 03:38:57
Claire Cury, Joan Glaunés, Roberto Toro, Gunter 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⟩