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Hypothesis testing with nonlinear shape models.

Timothy B Terriberry1, Sarang C Joshi, Guido Gerig

  • 1Dept. of Computer Science, Univ. of North Carolina, Chapel Hill, NC 27599, USA. tterribe@cs.unc.edu

Information Processing in Medical Imaging : Proceedings of the ... Conference
|March 16, 2007
PubMed
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We developed a new statistical test for comparing shapes using nonlinear models. This method accurately analyzes shape differences, even with complex data, and was applied to study brain ventricle shape in twins.

Area of Science:

  • Biomedical Engineering
  • Statistical Genetics
  • Computational Anatomy

Background:

  • Statistical shape analysis is crucial for understanding anatomical variation.
  • Nonlinear shape models offer flexibility in representing complex anatomical structures.
  • Assessing shape differences in genetically related individuals requires robust statistical methods.

Purpose of the Study:

  • To introduce a novel multivariate permutation test for two-sample hypothesis testing in statistical shape analysis.
  • To demonstrate the invariance of the proposed test to model parameter scales and its ability to handle variable dependencies.
  • To apply the method to analyze shape variability in the lateral ventricles of twins with varying genetic similarity.

Main Methods:

  • Development of a true multivariate permutation test tailored for nonlinear shape models.

Related Experiment Videos

  • Application of the test to medial representation (m-rep) models of the lateral ventricles.
  • Analysis of shape differences in relation to genetic similarity in twin cohorts.

    • Main Results:

    • The proposed permutation test effectively handles dependencies between shape model parameters.
    • The method is invariant to the scale of different model parameters, ensuring robust comparisons.
    • Significant shape variability was observed in lateral ventricles, with patterns potentially linked to genetic factors in twins.

    Conclusions:

    • The presented hypothesis testing method provides a powerful tool for statistical shape analysis with nonlinear models.
    • This approach facilitates the investigation of anatomical variability in relation to genetic and environmental factors.
    • The findings highlight the utility of advanced statistical methods in understanding complex biological structures like the brain ventricles.