Transcript A longitudinal study of brain development in autism Heather Cody Hazlett, PhD

NA-MIC
National 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
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
UNC DBP-2 Team:
•DBP-2
Co-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
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
Project: Cortical thickness analysis
of pediatric brain
Project 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
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
Regional cortical thickness
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
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
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
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)
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
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)
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
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.
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
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)
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
Project Workload Timeline
Completed:
• 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)
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
Downloads
Executable and tutorial dataset:
http://www.nitrc.org/projects/arctic/
Pediatric atlas:
http://www.insightjournal.org/midas/item/view/2277
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
Local cortical thickness
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
Local Cortical Thickness - Pipeline Overview
Input: Raw T1-weighted, T2-weighted, or PD-weighted
images
Eleven steps in the pipeline:
1. Tissue segmentation
2. Atlas-based ROI segmentation
3. White matter map creation
4. White matter map post-processing
5. Genus zero white matter map image &
surface creation
6. Gray matter map creation
7. White matter surface inflation
8. Cortical correspondence
9. Label map creation
10. Cortical thickness
11. Group statistical analysis
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
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)
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
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)
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
Local cortical thickness pipeline:
Step 3: White matter map creation
• Brainstem and cerebellum extraction
• Adding subcortical structures (except amygdala &
hippocampus)
• Tool: ImageMath (NITRC module)
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
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)
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
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
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
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)
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
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
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
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
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
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)
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
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 highlevel module using BatchMake
• Workflow for group analysis
NA-MIC
National Alliance for Medical Image Computing
http://na-mic.org
Contributors:
Joe Piven, MD
Guido Gerig, PhD
Martin Styner, PhD
Clement Vachet, MS
Cedric Matthieu, BA
Rachel Smith, BA
Mike Graves, MChE
Sarah Peterson, BA
Matt Mosconi, PhD
NA-MIC Team
Jim Miller
Ipek Oguz
Nicolas Augier
Marc Niethammer
Brad Davis
Parent grant funded by the
National Institutes of Health