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Analysis of Longitudinal Shape Variability via Subject Specific Growth Modeling James Fishbaugh 1 Marcel Prastawa 1 Stanley Durrleman 2 Joseph Piven 3.

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Presentation on theme: "Analysis of Longitudinal Shape Variability via Subject Specific Growth Modeling James Fishbaugh 1 Marcel Prastawa 1 Stanley Durrleman 2 Joseph Piven 3."— Presentation transcript:

1 Analysis of Longitudinal Shape Variability via Subject Specific Growth Modeling James Fishbaugh 1 Marcel Prastawa 1 Stanley Durrleman 2 Joseph Piven 3 Guido Gerig 1 1 Scientific Computing and Imaging Institute, University of Utah 2 INRIA/ICM, Pitié Salpêtrière Hospital, Paris, France 3 Carolina Institute for Developmental Disabilities, University of North Carolina

2 Longitudinal Design 1.112.6 0.7 13.424.2 0.8 12.824.4 24.8 0.6 Time (months)

3 The Problem Compare trajectories of shape change 610121824 Time (months) Trajectory A Trajectory B Normative Growth

4 Cross-sectional Extension of kernel regression to Riemannian manifolds [ Davis ICCV 2007 ] Piecewise-geodesic regression for image time-series [ Khan & Beg ISBI 2008 ] Geodesic regression [ Niethammer MICCAI 2011, Fletcher MICCAI MFCA 2011 ] Regression based on stochastic perturbations of geodesic paths [ Trouvé & Vialard Quarterly of Applied Mathematics 2012 ] Regression based on twice differentiable flows of deformation [ Fishbaugh MICCAI 2011 ] Longitudinal atlas construction Individuals modeled as spatiotemporal deformations from mean scenario [ Durrleman MICCAI 2009 ] Longitudinal atlas construction for DTI [ Hart MICCAI STIA 2010 ] and images [ Liao NeuroImage 2012 ] Background Davis ICCV 2007Niethammer MICCAI 2011Trouvé & Vialard QAM 2012 Durrleman MICCAI 2009 Liao NeuroImage 2012

5 Method Overview Framework consists of 3 main steps Estimation of a growth model by shape regression Method Overview

6 Concept: Time-indexed discrete shapes → continuous growth model Temporally smooth evolution based on controlled acceleration [ Fishbaugh MICCAI 2011 ] Shape Regression

7 Time Reference population time-series Estimate Atlas

8 4D model of normative evolution Estimate Atlas velocity (mm/month) 0 25

9 Personalized 4D models for subjects in different groups Group BGroup A Reference Atlas Estimate Individual Trajectories

10 Surface matching [ Vaillant & Glaunes IPMI 2005 ] at specified time → homologous space for statistical analysis Group AGroup B Reference Atlas At Time t i Warp Atlas to Individuals

11 Flow of diffeomorphisms are geodesic → initial momenta parameterize deformation Group AGroup B Statistics on Diffeomorphisms Reference Atlas At Time t i

12 Experiments Synthetic database of longitudinal shapes 12 subjects in each group by randomizing growth Experimental Validation 610121824 Time (months) Group A Group B Normative Growth

13 PCA on momenta that warp atlas to each individual in Group A Hypothesis testing on magnitude of initial momenta that map reference atlas to individuals (Bonferroni correction) Experimental Validation

14 Clinical database of longitudinal shapes: Study of early brain development in autism High risk recruits: Siblings diagnosed with autism High risk & controls scanned at 6, 12, and 24 months Structures: left/right hemisphere and cerebellum Three groups: HR+: 15 high risk subjects with positive ADOS HR-: 40 high risk subjects with negative ADOS LR-: 14 low risk controls with negative ADOS 6 12 24 Time (months) Clinical Application

15 PCA showing first mode of variability per age group Hypothesis testing → no significant differences between groups at any time point Clinical Application

16 A new approach for : Analyzing longitudinal shape variability Quantifying spatiotemporal population differences Future work: Longitudinal information in atlas construction Utilize rate of change, velocity/acceleration Further clinical applications (e.g. Huntington's disease) Conclusion Time point 1Time point 2Time point 3

17 This work was supported by: Acknowledgments NIH grant U54 EB005149 NA-MIC NIH grant RO1 HD055741 Autism Center of Excellence Project IBIS


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