NA-MIC National Alliance for Medical Image Computing UNC Shape Analysis Martin Styner, Ipek Oguz Department of CS UNC Chapel Hill Max

NA-MICNational Alliance for Medical Image Computing http://na-mic.org

UNC Shape Analysis

Martin Styner, Ipek Oguz

Department of CS

UNC Chapel HillMax Jacob Styner

National Alliance for Medical Image Computing http://na-mic.org Slide 2

UNC Shape Analysis• UNC Shape Analysis Toolbox

– SPHARM-PDM, Hotelling, permutation, FDR– Local shape analysis via MANCOVA– Shape analysis via discrimination (with MIT)– Collaborations (Utah, GT)– Over 100 downloads of shape tool distribution

• Enhanced correspondence: Curvature MDL• Ongoing Slicer 3 integration

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Binary Segmentation

Volumetric analysis: Size, Growth

Shape Representation Statistical analysis

Local processes


Segmentation

SphericalParameterization

SPHARM-PDMHotelling T2 metricSurface Distance

Hypothesis TestingPermutations, FDR,GLM+MANCOVA

Representation

Preprocessing

- Correspondence- Alignment- Scaling

Analysis

UNC Shape Analysis Toolbox


UNC Shape Analysis Toolbox

• Publications: MICCAI 06 (2x), ISBI 06, SPIE 07, sub. ISBI 07

• Comprehensive including visualization• Spherical harmonics + PDM• Complex shape: Striatum, from

subdivision to local shape analysis• NAMIC core interaction

– 1: Parts of analysis with GT, Utah– 3: Harvard PNL

• Caudate studies• Paper in preparation


Local Shape with Mancova• Current analysis only allows direct group

comparisons• No corrections for age, gender, weight etc• No correlation with variables, such as IQ, clinical scores,

age, duration of illness etc

• Work with D Pantazis, USCTest locally and permutation tests for correction1. General Linear Model fitting (for each x,y,z)2. MANCOVA model, Wilks’s & Roy’s Lambda3. Permutation tests over Test statistics

• Matlab implementation at USC• Application to UNC DBP Autism data drives

research • (correction for gender, age, IQ)


Shape Discrimination

• Shape analysis via discrimination– How to best discriminate 2 groups– Discrimination direction (DD), linear or radial basis function

• Application– Distance maps: Golland, MedIA 05– SPHARM-PDM surfaces– Good agreement hypo test and DD magnitude

• MIT, Kitware

MIT, Kitware, UNC

Rbf DD (solid)

SPHARMHypothesis


MDL Correspondence with Local FeaturesIpek Oguz, Martin Styner, Tobias Heimann, Guido Gerig

• Traditional MDL uses position to establish correspondence

• Not satisfactory for objects with complicated geometry

• We incorporate local features (e.g. curvature) to improve correspondence

Striatum (caudate + nucleus accumbens + putamen ), coloring is spherical parametrization


Criteria for Model Validation

• Compactness– Ability to use a minimal set of parameters

• Generalization– Ability to describe instances outside of

training set: leave one out

• Specificity– Ability to represent only valid instances of

the objects: Distance to closest sample


Results - I

• Simple object geometry

• SPHARM and MDL on pure curvature (CS) perform poorly

• MDL over Curvature + position (XYZCS) gives results similar to position (XYZ) only


Results - II

• Complex object geometry

• SPHARM and pure curvature (CS) performs poorly

• Curvature + position (XYZCS) gives better results than position only (XYZ)


Discussion Methodology

• With compex object geometry – local curvature improves correspondence

• Choice of particular curvature metric does not have significant effect– Principal curvatures, Gaussian curvature,

mean curvature, curvedness, shape index• Our framework can be used for any

combination of local features: local curvature, cortical thickness, fMRI, DTI, MRA, etc.

• MICCAI 2007 submission


Slicer 3 Integration

• External modules for all shape analysis tools in UNC pipeline– Individual modules– Visualization tool – No module for MDL

• Processing possible– Very tedious– Case by case, step by step…


Slicer 3 Modules


Next: All-In-One tool

• Batch processing is necessary for shape analysis from a practical viewpoint

• Top-level tool for whole shape analysis pipeline– GUI: intuitive, end-user in mind, Slicer 3

external module– Specification of input segmentations– Full shape pipeline computation

• Use of BatchMake for computing• Distributed computing with Condor (BatchMake)

– Advanced parameters for experts

NA-MICNational Alliance for Medical Image Computing http://na-mic.org

Future Development: Cortical Correspondence

Ipek Oguz, Martin Styner – UNC

Josh Cates, Tom Fletcher, Ross Whitaker – Utah


Main Idea - Cort Corresp

• Use entropy-based particle system (Cates) for cortical correspondence– Highly convoluted surface

• Integrate sMRI, DTI, MRA, fMRI– How to combine these data

• Single, flexible framework for the cortical surface, subcortical structures and cerebellum


Finding Correspondence

• In order to apply the particle method to the cortex, we need to first ‘inflate’ the surface

• Possible methods:– FreeSurfer– Area preserving surface evolution

(Tannenbaum ?, Faugeras ?, ..)


Integrating Data

• Structural– Position, curvature, depth to inflated surface

• Vascular – Distance to closest vessel(s) of certain size– Distance to labeled vessel(s)

• DTI– Probabilistic connectivity– To given region(s), intra & inter hemispheric– Locally reduced using priors/thresholds

• Local vascular & connectivity patterns


Example 1

• Targeting fMRI –better functional correspondence (better sensitivity) in an amygdala-curcuit related task

• MRA data: distance to closest arterial vessel of minimal size (2mm)

• DTI data: connectivity to amygdala


Example 2

• Cortical thickness comparison with better “anatomic” correspondence

• MRA: distance to major vessels (arterial & venal)

• DTI: probabilistic connectivity to all major subcortical structures – Connectivity vector– Possibly train & threshold

Documents

NA-MIC National Alliance for Medical Image Computing UNC Shape Analysis Martin Styner, Ipek Oguz Department of CS UNC Chapel Hill Max