Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets

Stanley Durrleman; Xavier Pennec; Guido Gerig; Alain Trouvé; Nicholas Ayache

inria-00408293, version 1

Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets

Stanley Durrleman ( Author to contact preferably ) ^1, 2, Xavier Pennec () ¹, Guido Gerig^a^, 3, Alain Trouvé () ², Nicholas Ayache () ¹

N° RR-6952 (2009)

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.

a – University of Utah
1: ASCLEPIOS (INRIA Sophia Antipolis)
INRIA
2: Centre de Mathématiques et de Leurs Applications (CMLA)
CNRS : UMR8536 – École normale supérieure de Cachan - ENS Cachan
3: Scientific Computing and Imaging Institute (SCI Institute)
University of Utah

inria-00408293, version 1
http://hal.inria.fr/inria-00408293
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From: Stanley Durrleman <>
Submitted on: Thursday, 30 July 2009 10:47:47
Updated on: Wednesday, 9 September 2009 11:12:26

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%% inria-00408293, version 1
%% http://hal.inria.fr/inria-00408293
@techreport{durrleman:inria-00408293,
    hal_id = {inria-00408293},
    url = {http://hal.inria.fr/inria-00408293},
    title = {{Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets}},
    author = {Durrleman, Stanley and Pennec, Xavier and Gerig, Guido and Trouv{\'e}, Alain and Ayache, Nicholas},
    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.}},
    keywords = {Computational Anatomy ; longitudinal data ; spatiotemporal variability ; 4D registration ; time regression ; atlas construction ; large deformation ; diffeomorphism ; currents},
    language = {English},
    affiliation = {ASCLEPIOS - INRIA Sophia Antipolis , Centre de Math{\'e}matiques et de Leurs Applications - CMLA , Scientific Computing and Imaging Institute - SCI Institute},
    pages = {17 pages},
    type = {Research Report},
    institution = {INRIA},
    number = {RR-6952},
    year = {2009},
    pdf = {http://hal.inria.fr/inria-00408293/PDF/RR-6952.pdf},
}

%F inria-00408293, version 1
%0 Report
%U http://hal.inria.fr/inria-00408293
%2 INFO:INFO_TS;SPI:SIGNAL
%T Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets
%A Durrleman, Stanley
%A Pennec, Xavier
%A Gerig, Guido
%A Trouvé, Alain
%A Ayache, Nicholas
%+ ASCLEPIOS - INRIA Sophia Antipolis , Centre de Mathématiques et de Leurs Applications - CMLA , Scientific Computing and Imaging Institute - SCI Institute
%X 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.
%2 I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.1: Models/I.5.1.4: Statistical, I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.2: Design Methodology/I.5.2.2: Pattern analysis, I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.7: Feature Measurement/I.4.7.4: Size and shape, I.4.8.11: Time-varying imagery
%G English
%2 Research Report
%P 17 pages
%8 2009-00-00
%K Computational Anatomy ; longitudinal data ; spatiotemporal variability ; 4D registration ; time regression ; atlas construction ; large deformation ; diffeomorphism ; currents
%Z RR-6952

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