framework for the statistical shape analysis of brain structures using spharm-pdm
DESCRIPTION
Framework for the Statistical Shape Analysis of Brain Structures using SPHARM-PDM. M. Styner , I. Oguz, S. Xu, C. Brechbuehler, D. Pantazis, J. Levitt, M. Shenton, G. Gerig UNC, ETHZ, USC, Harvard, NA-MIC. Brain Morphometry. Brain Morphometry in Neurological Disorders - PowerPoint PPT PresentationTRANSCRIPT
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Framework for the Statistical Shape Analysis of Brain
Structures using SPHARM-PDM
M. Styner, I. Oguz, S. Xu, C. Brechbuehler, D. Pantazis, J. Levitt, M. Shenton, G. Gerig
UNC, ETHZ, USC, Harvard, NA-MIC
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Brain Morphometry
• Brain Morphometry in Neurological Disorders– Morphometry Pathology– Schizophrenia, Autism, Alzheimer’s, Depression, MPS,
Krabbe, FragileX
GroupDifference
SZ Cnt
Difference
Stats
Difference
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Concept: Shape Analysis• Group analysis of a brain region• Traditional analysis: only regional volume• Additional shape analysis via SPHARM PDM
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
Binary Segmentation
Volumetric analysis: Size, Growth
Shape Representation Statistical analysis
Local processes
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Table of Contents
• Motivation: – Brain morphometry
• Methodology: – SPHARM PDM– Statistical Testing
• Tool development• Example
– Caudate shape in Schizo-typal Personality Disorder (PSD)
• Discussion & Outlook
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Segmentation
SphericalParameterization
SPHARM-PDM
Hotelling T2
Surface Distance
StatisticalHypothesis Testing
Representation
Preprocessing
- Correspondence- Alignment- Scaling
Analysis
Shape Analysis Workflow
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Representation: SPHARM-PDM
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• Hierarchical description• Spherical harmonics basis1. Surface & Parameterization2. Fit coefficients of parameterized
basis functions to surface3. Reconstruct object PDM
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Representation: SPHARM-PDM• Correspondence by
parameterization– First order ellipsoid
• Initialization for other methods– Prior talk Heimann, Oguz
• IPMI 2003 comparison• Alignment
– Rigid-Body Procrustes to template
• Normalization with uniform scaling:– Original size: as is– Cranial cavity size normalization– User choice
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Group Shape Difference
• Corresponding aligned surfaces• Analyze shape differences
– Features per surface point– Multivariate: Point locations– Hotelling T2 two sample metric
• At each location: Hypothesis test– Difference between groups?– P-value of group mean difference– Significance map
• Non-parametric permutation tests– No distribution assumption
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P-value Correction
• Many tests computed independently– Biased, highly optimistic
• Corrected significance map– As if only one test performed
• Bonferroni correction– Global False-Positive rate, simple– Very pessimistic– pcorr = p/n = 0.05/1000 = 0.00005
• Non-parametric permutation tests– Minimum statistic of raw p-values– Global False-Positive rate– Still pessimistic
• False Discovery Rate– Allow an expected rate of falsely
significant tests
ISBI 2004 Pantazis, Leahy, Nichols, Styner
Correction
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Tool Development
• Methodology clinically useful tools• Computer scientists create tools• Our shape analysis tools:
– Enable clinical investigators to create knowledge– In use: Harvard (BWH, VAB), NIMH, Duke (CIVM, NIRL), UIUC,
GeorgiaTech, UUtah, U. Bern, U. Zaragoza, ANU Canberra, UNC
– Open Source, UNC NeuroLib, Tested, Validated– CVS download and linux binaries with examples
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Shape Analysis Tools I
• Command line– Scripting simple
• SegPostProcess– Spherical Topology– Smoothing– Up-interpolation– Interior filling
• GenParaMesh– Surface Mesh– Spherical
Parameterization• Brechbuehler CVGIP
Segmentation: e.g. using InsightSNAPOutput: Binary 3D Image
Parameterization: GenParaMeshOutput: Surface Mesh + Parameterization
SPHARM-PDM: ParaToSPHARMMeshOutput: SPHARM + Aligned Surface
Preprocessing: SegPostProcessOutput: Binary 3D Image
For Each Datasets
Statistical Testing: StatNonParamPDMOutput: Significance + Descriptive Maps
For Each Comparison
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Shape Analysis Tools II
• ParaToSPHARMMesh– SPHARM-PDM– Alignment
• StatNonParamPDM– Descriptive Statistics
• Mean, Variance
– Significance Map• Raw, Corrected
• Examples, Scripts• Many parameters
– See manuscript
Segmentation: e.g. using InsightSNAPOutput: Binary 3D Image
Parameterization: GenParaMeshOutput: Surface Mesh + Parameterization
SPHARM-PDM: ParaToSPHARMMeshOutput: SPHARM + Aligned Surface
Preprocessing: SegPostProcessOutput: Binary 3D Image
For Each Datasets
Statistical Testing: StatNonParamPDMOutput: Significance + Descriptive Maps
For Each Comparison
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Example Caudate Shape
• Right Caudate– Basal Ganglia structure– Schizo-typal Personality
Disorder (15 subjects)– Controls (14 subjects)– Male subjects only
• Segmentation with 3D Slicer v2 (BWH)
QuickTime™ and aMPEG-4 Video decompressor
are needed to see this picture.
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Caudate Study
• Correspondence– KWMeshVisu
• Descriptive Statistics
QuickTime™ and aMPEG-4 Video decompressor
are needed to see this picture.
Covariance ellipsoids Mean DifferenceMedial Lateral
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Caudate Study
• Hypothesis testing– Levels of correction
• Global shape difference
– Mean difference p = 0.009
• Right caudate different between Cnt and SPD
• Interpretation by clinicians
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Discussion
• Comprehensive set of open source tools for shape analysis using SPHARM-PDM– Command line tools– Local group differences– Applied in UNC studies: Twin similarity,
Schizophrenia, Autism, Fragile-X
• Visualization: – Quality Control is important– KWMeshVisu: prior talk Oguz
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Outlook
• MANCOVA for group variables– Age, gender, clinical scores
• Open hippocampus dataset for testing• Testing environment for other data
– Deformation field– Cortical thickness data
• Questions?• Support:
– National Alliance for Medical Image Computing, NIH Roadmap Grant U54 EB005149-01– UNC Neurodevelopmental Disorders Research Center HD 03110– NIH NIBIB grant P01 EB002779, EC-funded BIOMORPH project 95-0845, VA Merit Award,
VA Research Enhancement Award Program, NIH R01 MH50747, K05 MH070047
NA-MIC
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Humans• Large Variability
Monkey• Reduced complexity
and variability
Mouse• Genetic control• Small variability• No folding
TranslationalResearch
Brain Morphometry
• Studies of normal development
• Studies in animals
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CVS and Dashboard
Doxygen
• CVS repository for source, nightly compilation and testing
• Code/Dashboard master
Dashboard
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Statistical Hypothesis Testing
• At each location: Hypothesis test– Significant difference between groups?– P-value of group mean difference
• Schizophrenia group vs Control group
– Significance map– Threshold α, e.g. 5%
• Non-parametric permutation tests– No distribution assumption– P-values directly from observed distribution
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Permutation Hypothesis Tests
• Estimate distribution– Permute group labels
• Na , Nb in Group A and B
• Create M permutations
• Compute feature Sj for each perm
• Histogram Distribution• p-value:
#Perms larger / #Perms total
S0
Sj
Sj
perm
#
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SPHARM Parameterization
• Spherical topology of segmentation
• Mapping of surface to unit sphere– Difficult, no unique ordering of points in 3D– Initialize with heat equation mapping– Optimization for equal area ratio mapping
with minimal angular distortion
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Example: Hippocampus in SZ
• Temporal lobe, Limbic system• Storage of auditory and visual
memories• 56 Schizophrenics vs 26 Controls• Surface difference• Main differences at tail
Styner, Lieberman, Pantazis, Gerig: Boundary and Medial Shape Analysis of the Hippocampus in Schizophrenia, Medical Image Analysis, 2004, pp 197-203Styner, Lieberman, Gerig: Boundary and Medial Shape Analysis of the Hippocampus in Schizophrenia, MICCAI 2003, II, pp. 464-471
Diff between Means
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UNC Shape Analysis
• Group analysis of a brain region
• Regional volume and shape analysis
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
Binary Segmentation
Volumetric analysis: Size, Growth
Shape Representation Statistical analysis
Local processes
GroupDifference
SZ Cnt
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UNC Shape Analysis
• UNC Open Source– Comprehensive set of analysis tools– Visualization tools
• Separate talk later