Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets

Abstract : We propose a new methodology to analyze the anatomical variability of a set of longitudinal data (population scanned at several ages). This method accounts not only for the usual 3D anatomical variability (geometry of structures), but also for possible changes in the dynamics of evolution of the structures. It does not require that subjects are scanned the same number of times or at the same ages. First a regression model infers a continuous evolution of shapes from a set of observations of the same subject. Second, spatiotemporal registrations deform jointly (1) the geometry of the evolving structure via 3D deformations and (2) the dynamics of evolution via time change functions. Third, we infer from a set of subjects a prototype scenario of evolution and its 4D variability within the population. Our method is used to analyze the morphological evolution of 2D profiles of hominids skulls and to analyze brain growth from amygdala of autistics, developmental delay and control children.
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[Research Report] RR-6952, INRIA. 2009, 17 p
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Contributeur : Stanley Durrleman <>
Soumis le : jeudi 30 juillet 2009 - 10:47:47
Dernière modification le : vendredi 12 janvier 2018 - 01:55:36
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  • HAL Id : inria-00408293, version 1


Stanley Durrleman, Xavier Pennec, Guido Gerig, Alain Trouvé, Nicholas Ayache. Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets. [Research Report] RR-6952, INRIA. 2009, 17 p. 〈inria-00408293〉



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