National Alliance for Medical Image Computing Structure.

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

Structure


Core 1: OverviewHarvard

Georgia TechUNC

UtahMIT

Segmentation

Registration

Foundational Methods

Structural Features and Statistics

Connective Features and Statistics

1. Shape and Atlas Based Segmentation

2. Statistical Shape Analysis

3, DTI Connectivity Analysis

1. Diffusion-based Registration

2.Group Effect Maps

3. Automatic Segmentation

1. DTI Processing

2. Surface Processing

3. PDE Implementations

1. Combined Statistical/PDE Methods1. Quantitative DTI Analysis

2. Cross-Sectional Shape Analysis2. Stochastic Flow Models

Figure 3: a) A rendering of a cortical surface, extracted from MRI, shows a degree of noise that significantlyaffects successive processing. b) A feature-preserving, PDE-based filter smooths away small-scale noisewhile preserving sharp features such as the concave regions of the sulci.

(a) (b)


Core 1: Overview

• Computational tools for image analysis– Extract anatomical structures at many

scales – Measure properties of extracted

structures– Determine connectivity between

extracted structures– Relate disease factors to

measurements


Shape Analysis

• Developing pipeline protocols for population comparisons, jointly with UNC.

• Integrating discriminative analysis into the pipeline:– Shape-based classification


EM Segmentation with Non-Stationary Tissue Priors

• Integrating into Slicer

M-Step

E-Step

Bias:Predict Error

Image

Correct Intensities

MF-Step:Regularize Weights

Estimate TissueProbability

Label Map


Already in NAMIC Software

• Shape prior for segmentation– Leventon 2001– Added to ITK by others

• DTI visualization– O’Donnell (CSAIL), LMI

(BWH)– In VTK-based 3D Slicer


Future work (6 months)

• Complete shape-based segmentation implementation– Insert into toolkit

• Shape based comparison and population analysis– Structural components– Tract components


Q-Ball Imaging in Slicer

Estepar, Snyder, Kindlmann, Westin


Automatic Thalamus Segmentation

LGNMGN

VLMD

VA

VLMD

VA

CM

Pu Pu

CM

VL

VA

MD

Ziyan, Tuch


1. Make QBALL availableCheck QBALL code into Slicer and VTK.

2. Does nonlinear registration boost stats?Measure power benefit of ITK nonlinear registration for FA group comparisons.

3. Are group comparisons based on the full tensor more sensitive?Implement and measure sensitivity of tensor-based group comparison method.

Future Work


Utah Core 1 Activities

• Differential Geometry for DTI analysis

• Descriptive statistics of DTI

• Hypothesis testing DTI

• Interpolation and filtering of DTI


Curved Tensor Geometry

• Natural geometry for tensor analysis

• Enforces positive eigenvalues

• Basis for statistics, interpolation, and processing

Space of 2x2 tensors: a bb c


Descriptive Statistics

• Averages and Modes of Variation

• Preserves natural properties

–Positive eigenvalues

–Tensor Orientation

–Tensor Size (determinant)

• Prototype implemented in ITK


Hypothesis Testing

• Tests differences in diffusion tensors from two groups

• Uses full six-dimensional information from tensors

• Prototype implemented in ITK

• Upcoming IPMI submission


Interpolation and Filtering

• Interpolation of tensors

–Based on weighted averages in curved geometry

• Filtering

–Anisotropic filtering based on curved geometry

• Implementation in progress


Statistics Processing

Software

• Different tensor geometries can be defined

• Each package can swap in/out different geometries

TensorGeometry

LinearGeometryCurvedGeometry

Other?

DescriptiveStatsTensorGeometry

HypothesisTestsTensorGeometry

InterpolationTensorGeometry

FilteringTensorGeometry


Future Work (6 months)

• Further develop tensor statistics—make publicly available

• Build prototypes of tensor filtering and interpolation

• Continue research into DTI hypothesis testing

–Methods

–Exploratory Experiments


UNC: Quantitative DTI Analysis

Guido Gerig, Isabelle Corouge

Students: Casey Goodlett and Clement Vachet


Conventional Analysis: ROI or voxel-based group tests after alignment

Patient

Control

Quantitative DTI Analysis

UNC NA-MIC Approach:

• Quantitative Analysis of Fiber Tracts

• DTI Tensor Statistics across/along fiber bundles

• Statistics of tensors

Tracking/

clustering

selection

FA FA along tract


Example: Fiber-tract Measurements

Corouge, Isabelle, Gouttard, Sylvain and Gerig, Guido, "Towards a Shape Model of White Matter Fiber Bundles using Diffusion Tensor MRI" , Proc. IEEE Computer Society, Int. Symp. on Biomedical Imaging, to appear April 2004

Gerig, Guido, Gouttard, Sylvain and Corouge, Isabelle, "Analysis of Brain White Matter via Fiber Tract Modeling", full paper IEEE Engineering in Medicine and Biology Society EMBS 2004, Sept. 2004

uncinate fasciculus

uncinate fasciculus

FA along uncinate

cingulum FA along cingulate

Major fiber tracts


Processing Steps

• Tractography– Data structure for sets of

attributed streamlines

• Clustering• Parameterization• Diffusion properties

across/along bundles• Graph/Text Output• Statistical Analysis

Slicer (?) ITK DTI Fiber Spatial

Object data structure (J. Jomier)

Normalized Cuts (ITK) B-splines (ITK) NEW: DTI stats in

nonlinear space (UTAH) Display/Files Biostatistics / ev. DTI

hypothesis testing (UTAH)


Results

FiberViewer Prototype (ITK)

• Clustering (various metrics)

• Parametrization• FA/ADC/Eigen-value

Statistics• Uses SpatialObjects and

SpatialObject-Viewer• Used in two UNC clinical

studies (neonates, autism)• Validation: ISMRM’05


Next 6 months

• Methodology Development:– DTI tensor statistics: close collab. with UTAH– Deliver ITK tools for clustering/parameterization to Core 2– Feasibility tests with tractography from Slicer– Deliver prototype platform to Core 2 to discuss integration

into Slicer

• Clinical Study: DTI data from Core 3– Check feasibility of tract-based analysis w.r.t. DTI resolution

(isotropic voxels(?)), SNR– Apply procedure to measure properties of:

• Cingulate (replicate ROI findings)• Uncinate fasciculus (replicate ROI findings)• Other tracts of interest


UNC: Statistical Shape Analysis

Martin Styner

Students: Ipek Oguz and Christine Shun Xu


Shape Analysis Pipeline• Clinical need: Localization of shape and volume

changes• 3D objects of spherical topology• Input: Segmentation from models or binary

images• Modeling Steps:

– Individual surface models• Regularization• Correspondence

– Alignment via Procrustes & choice of scale– Skeletal description

• Structural subdivision• Statistical analysis of models


Shape Analysis Pipeline

• Thickness maps – Distance to skeleton

• Local shape analysis– To template or template-free– Univariate Euclidean distance– Multivariate Hotelling T2 distance– Raw p values, t/T2-maps, effect-size– Conservative correction for Type II error

• MIT discriminative analysis complements our shape analysis well

• Visualizations of steps for QC


Next 6 months• NAMIC toolkit development

– Standardization of IO & internal representation• With MIT & Georgia Tech

– Standardization of visualization tools– Automation of tools, transfer to standard

• Methodology development– Non-Euclidean shape metrics with permutation

tests– Probabilistic structural subdivision method– 3D visualization maps of statistical metrics

• Clinical: Shape analysis data from Core 3– Feasibility of shape analysis on data from Core 3– Caudate shape analysis on Brockton VA/Harvard

data


Georgia Tech

Ramsey Al-Hakim Steven Haker

Delphine Nain

Eric Pichon

Allen Tannenbaum


Anisotropic active contours

• Add directionality


Curve minimization

• Calculus of variations– Start with initial curve– Deform to minimize energy– Steady state is locally optimum

• Dynamic programming– Choose seed point s– For any point t, determine globally

optimal curve t s

Registration,Atlas-basedsegmentation

Segmentation


Synthetic example (3D)


L2 Bases Functions Local Shape Analysis

Our goal is to build more localized shape priors that can handle surfaces with high frequencies (high curvatures) and learn the local variations from the training set.

We propose to compare different L2 bases. In particular, we would like to investigate the use of multiscale shape analysis and learn localized shape statistics from the data using bayesian statistics.

The applications are shape prior for segmentation, registration and classification.


Example: Some Local Variations

Finding local variations in Prostate data at different frequency levels and spatial locations

Low Frequency

High Frequency


Segmentation of Area 46 Using Fallon’s Rules-I

Ramsey Al-HakimBME Undergraduate


Segmentation of Area 46 Using Fallon’s Rules-II

Ramsey Al-HakimBME Undergraduate


Work in Next Six Months

• Choice of anisotropic conformal factors for DTI-tractography.

• Comparison of L2 bases for shape analysis (application to caudate).

• Making Fallon’s rules more automatic for segmentation.


Structure

National Alliance for Medical Image Computing Structure.

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