Framework for the Statistical Shape Analysis of Brain Structures using SPHARM-PDM.

By Martin Andreas Styner, I. Oguz, Shun Xu, Christian M. Brechbühler, Dimitrios Pantazis, J. Levitt, Martha Elizabeth Shenton, Guido Gerig. The insight journal, 2006

491 citations recorded by the source corpus.

Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology.The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM). After alignment, differences between groups of surfaces are computed using the Hotelling T(2) two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information.The correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem. The first one aims to control the family-wise error rate (FWER) or false-positives via the extrema histogram of non-parametric permutations. The second method controls the false discovery rate and results in a less conservative estimate of the false-negatives.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Geometry and Topology

View Paper: https://www.semanticscholar.org/paper/ab51bce59de790cc33ef7fac6468bcbaac3447b8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Geometry and Topology

Citations in the corpus

Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging (2009, unknown): “To test for group differences in the spatial location (x,y,z) at each vertex of the hippocampal surface, we used the multivariate Hotelling T(2) metric (Styner et al., 2006).”
Sparse Shape Representation using the Laplace-Beltrami Eigenfunctions and Its Application to Modeling Subcortical Structures (2012, methodology): “In previous LB-eigenfunction and similar spherical harmonic (SPHARM) expansion approaches only the first few terms are used in the expansion and higher frequency terms are simply thrown away [21, 24, 28] to reduce the high frequency noise.”
A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes (2008, methodology): “Using the learned models, FIRST searches through shape deformations that are linear combinations of the modes of variation to find the most probable shape instance given the observed intensities from the input image.”
Geometric morphometrics: recent applications to the study of evolution and development (2010, methodology): “Spherical Fourier harmonic analysis, SPHARM, is a third approach that works on the same principal as the 2D outline Fourier method described above (Styner et al., 2006; McPeek et al., 2008).”