Abstract
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.
Highlights
The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate
Quantitative morphologic assessment of individual brain structures is often based on volumetric measurements
The spherical harmonic description (SPHARM) shape analysis approach was extended by [11] to use the implied sampled surface (SPHARM-Point Distribution Models (PDM)), a method used by [12]. [13], [14] and Golland [15] proposed shape analysis on medial shape descriptions in 3D and 2D, respectively. They used a fixed topology sampled model with implicit correspondence that is fitted to the objects. This manuscript presents a comprehensive set of tools that form a pipeline for the computation of 3D structural statistical shape analysis on the object boundary via SPHARM
Summary
Quantitative morphologic assessment of individual brain structures is often based on volumetric measurements. The shape analysis of densely sampled 3D Point Distribution Models (PDM) and their deformations was first investigated by [8] Inspired by their experiments, [9] proposed shape analysis based on a parametric boundary description called SPHARM ([10]). [13], [14] and Golland [15] proposed shape analysis on medial shape descriptions in 3D and 2D, respectively They used a fixed topology sampled model with implicit correspondence that is fitted to the objects. This manuscript presents a comprehensive set of tools that form a pipeline for the computation of 3D structural statistical shape analysis on the object boundary via SPHARM (see Figure 1). The main limitations of our shape analysis methodology is the necessity of spherical topology
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