NA-MIC National Alliance for Medical Image Computing A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Neurodevelopmental

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

A longitudinal study of brain development in autism

Heather Cody Hazlett, PhD Neurodevelopmental Disorders Research

Center& UNC-CH Dept of Psychiatry

NA-MIC AHM Salt Lake City, UT Jan 8, 2009


UNC DBP-2 Team:

•DBP-2Co-PI: Heather Cody Hazlett, PhD Co-PI: Joseph Piven, MD CS Programmers: Clement Vachet MS, Cedric Matthieu BA

•Core 1: Martin Styner, UNC Chapel Hill •UNC Algorithm: Ipek Oguz, Nicolas Augier, Marc Niethammer•Utah Algorithm: Marcel Prastawa

•Core 2: Jim Miller, GE Research


Project: Cortical thickness analysis of pediatric brainProject Goals:

– Individual and group analysis of regional and local cortical thickness

– Creation of an end-to-end application within Slicer3– Workflow applied to our large pediatric dataset

Why is this needed?

- Existing tools (e.g. FreeSurfer) are tailored to work with adult brain

- Pediatric brain shows more variability in brain shape and maturation (esp. white matter) than adult brain


Regional cortical thickness


Regional Cortical Thickness - Pipeline Overview

A Slicer3 high-level module for individual cortical thickness analysis has been developed:

ARCTIC (Automatic Regional Cortical ThICkness)

Input: raw data (T1-weighted, T2-weighted, PD-weighted images)

Three steps in the pipeline:1. Tissue segmentation 2. Regional atlas deformable registration 3. Cortical Thickness


Regional cortical thickness (ARCTIC) pipeline: Step 1: Tissue segmentation

• Probabilistic atlas-based automatic tissue segmentation via an Expectation-Maximization scheme

• Tool: itkEMS (UNC Slicer3 external module)


Regional cortical thickness (ARCTIC) pipeline:Step 2. Regional atlas deformable registration • 2.1 Skull stripping using previously computed tissue

segmentation label image

Tool: SegPostProcess (UNC Slicer3 external module)

•2.2 T1-weighted atlas deformable registration using a B-spline pipeline registration

Tool: RegisterImages (Slicer3 module)

•2.3 Applying transformation to the parcellation map

Tool: ResampleVolume2 (Slicer3 module)


Regional cortical thickness (ARCTIC) pipeline:Step 3. Cortical Thickness

• Sparse asymmetric local cortical thickness • Tool: CortThick (UNC Slicer3 module)

Note: All the tools used in the current pipeline are Slicer3 modules, some of them being UNC external modules.

The user can thus compute an individual regional cortical thickness analysis by running the 'RegionalCortThickPipeline' module, either within Slicer3 or as a command line.


ARCTIC Pipeline Validation

Analysis on a small pediatric dataset: Initial tests have been computed on a small pediatric dataset which includes 2 year-old and 4 year-old cases.

N = 16 with Autism, 1 with Dev Delay, 3 Typ Developing

Comparison to ‘state of the art’: ARCTIC vs. Freesurfer: We are currently doing a regional statistical analysis using Pearson's correlation coefficient on a dataset that includes ~ 90 cases and for two comparison groups (2 yr-old cases and 4 yr-old cases)


Project Workload TimelineCompleted:

• Workflow for individual analysis (Slicer3 external module using BatchMake)

• 2 Tutorials: "How to use the UNC modules to compute the regional cortical thickness" and "How to use ARCTIC"

In progress: •Pediatric atlases available to the community through MIDAS

•Comparison to FreeSurfer: pearson correlation analysis

•ARCTIC available to the community through NITRC: executables (UNC external modules for Slicer3), source code (SVN), and Tutorial dataset

Future work:•Workflow for group analysis (KWWidgets application using BatchMake)


Downloads

Executable and tutorial dataset:

http://www.nitrc.org/projects/arctic/

Pediatric atlas:

http://www.insight-journal.org/midas/item/view/2277

http://www.nitrc.org/projects/arctic/




Local cortical thickness


Local Cortical Thickness - Pipeline Overview

Input: Raw T1-weighted, T2-weighted, or PD-weighted images

Eleven steps in the pipeline: 7. White matter surface inflation 8. Cortical correspondence 9. Label map creation 10. Cortical thickness 11. Group statistical analysis

1. Tissue segmentation 2. Atlas-based ROI segmentation3. White matter map creation 4. White matter map post-processing 5. Genus zero white matter map image & surface creation 6. Gray matter map creation


Local cortical thickness pipeline: Step 1: Tissue segmentation

• Probabilistic atlas-based automatic tissue segmentation via an Expectation-Maximization scheme

• Tool: itkEMS (UNC Slicer3 external module)


Local cortical thickness pipeline: Step 2: Atlas-based ROI segmentation: subcortical structures, lateral ventricles, parcellation

2.1 T1-weighted atlas deformable registration

• B-spline pipeline registration

• Tool: RegisterImages (Slicer3 module)

2.2 Applying transformations to the structures

• Tool: ResampleVolume2 (Slicer3 module)


Local cortical thickness pipeline: Step 3: White matter map creation

• Brainstem and cerebellum extraction

• Adding subcortical structures (except amygdala & hippocampus)

• Tool: ImageMath (NITRC module)


Local cortical thickness pipeline: Step 4: White matter map post-processing

• Largest component computation

• White matter filling

• Smoothing: Level set smoothing or weighted average filter

• Connectivity enforcement (6-connectivity)

• Tool: SegPostProcessB (Slicer3 external module)


Local cortical thickness pipeline: Step 5: Genus zero white matter map image and surface creation

•Tool: GenusZeroImageFilter (UNC Slicer3 external module)

Step 6: Gray matter map creation

•Adding genus zero white matter map to gray matter segmentation (without cerebellum and brainstem)

•Tool: ImageMath


Local cortical thickness pipeline: Step 7: White matter surface inflation

• Iterative smoothing using relaxation operator (considering average vertex) and L2 norm of the mean curvature as a stopping criterion

• Fixing is necessary: remove vertices that have too high curvature (extremities)

• Tool: MeshInflation (UNC Slicer3 external module)


Local cortical thickness pipeline: Step 8: Cortical correspondence

• Correspondence on inflated surface using particle system

• Tool: ParticleCorrespondence (UNC Slicer3 external module)

Step 9: Label map creation

• Label map creation for cortical thickness computation (WM + GM + "CSF")

• Tool: ImageMath


Local cortical thickness pipeline: Step 10: Cortical thickness

• Asymmetric local cortical thickness or Laplacian cortical thickness

• Tool: UNCCortThick or measureThicknessFilter (UNC Slicer3 external modules)

Step 11: Group statistical analysis

• Tool: QDEC Slicer module or StatNonParamPDM


Pipeline validation

Analysis on a small pediatric dataset: (to be done)Tests will be computed on a small pediatric dataset which includes 2 year-old and 4 year-old cases.

N = 16 with Autism, 1 with Dev Delay, 3 Typ Developing

Comparison to ‘state of the art’: (ongoing)Pipeline vs. Freesurfer: We are currently doing a regional statistical analysis using Pearson's correlation coefficient on a dataset that includes ~ 90 cases and for two comparison groups (2 yr-old cases and 4 yr-old cases)


Project Workload Timeline

In progress

•Cortical surface inflation: module in progress

•Mesh needs to be fixed at some location to have a correct inflation

Future work

• Workflow for individual analysis as a Slicer3 high-level module using BatchMake

• Workflow for group analysis


Joe Piven, MDGuido Gerig, PhDMartin Styner, PhDClement Vachet, MSCedric Matthieu, BARachel Smith, BAMike Graves, MChESarah Peterson, BAMatt Mosconi, PhD

Parent grant funded by the National Institutes of Health

Contributors:NA-MIC Team

Jim MillerIpek OguzNicolas AugierMarc NiethammerBrad Davis

Documents

NA-MIC National Alliance for Medical Image Computing A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Neurodevelopmental