Statistical shape analysis of brain arterial networks (BAN)

Guo, X.; Bal, Aditi Basu; Needham, Tom; Srivastava, Anuj

doi:10.1214/21-aoas1536

Cited by 5 publications

(1 citation statement)

References 42 publications

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“…Other examples include analysis of unlabeled networks (Calissano et al, 2023), data on a Grassmannian (Hong et al, 2016), data of 3D rotations (Fletcher, 2013), compositional data (Pawlowsky-Glahn et al, 2015) and density data (van den Boogaart et al, 2014). Srivastava and Klassen (2016) also combines parametrization invariance with statistical shape analysis -to analyze shapes of curves and also surfaces -and, more recently, also with analysis of unlabeled graphs to model brain arterial networks (Guo et al, 2022).…”

Section: Introduction: Regression For Metric Spacesmentioning

confidence: 99%

Elastic Analysis of Irregularly or Sparsely Sampled Curves

2022

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We propose regression models for curve-valued responses in two or more dimensions, where only the image but not the parametrization of the curves is of interest. Examples of such data are handwritten letters, movement paths or outlines of objects. In the square-root-velocity framework, a parametrization invariant distance for curves is obtained as the quotient space metric with respect to the action of re-parametrization, which is by isometries. With this special case in mind, we discuss the generalization of 'linear' regression to quotient metric spaces more generally, before illustrating the usefulness of our approach for curves modulo re-parametrization. We address the issue of sparsely or irregularly sampled curves by using splines for modeling smooth conditional mean curves. We test this model in simulations and apply it to human hippocampal outlines, obtained from Magnetic Resonance Imaging scans. Here we model how the shape of the irregularly sampled hippocampus is related to age, Alzheimer's disease and sex.

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