Difference between revisions of "DBP2:UNC:Local Cortical Thickness Pipeline"

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:** Smoothing: Level set smoothing or weighted average filter
 
:** Smoothing: Level set smoothing or weighted average filter
 
:** Connectivity enforcement (6-connectivity)
 
:** Connectivity enforcement (6-connectivity)
−
:** Tool: SegPostProcessB (Slicer3 external module)
+
:** Tool: WMSegPostProcess (Slicer3 external module)
 
:* '''5. Genus zero white matter map image and surface creation'''
 
:* '''5. Genus zero white matter map image and surface creation'''
 
:** Tool: GenusZeroImageFilter (UNC Slicer3 external module)
 
:** Tool: GenusZeroImageFilter (UNC Slicer3 external module)
−
:* '''6. Gray matter map creation'''
+
:* '''6. White matter surface inflation'''
 +
:** Iterative smoothing using relaxation operator (considering average vertex) and L2 norm of the mean curvature as a stopping criterion
 +
:** Iteration stopped if vertices that have too high curvature (some extremities)
 +
:** Tool: MeshInflation (UNC Slicer3 external module)
 +
:* '''6 bis(Optional). White matter image fixing if necessary'''
 +
:** Correction of the white matter map image (corresponding to vertices that have high curvature)
 +
:** Tool: FixImage (UNC Slicer3 external module)
 +
:** Go back to step 5
 +
:* '''7. Gray matter map creation'''
 
:** Adding genus zero white matter map to gray matter segmentation (without cerebellum and brainstem)
 
:** Adding genus zero white matter map to gray matter segmentation (without cerebellum and brainstem)
 
:** Tool: ImageMath
 
:** Tool: ImageMath
−
:* '''7. White matter surface inflation'''
+
:* '''8. Label map creation'''
−
:** 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)
 
−
:* '''8. Cortical correspondence'''
 
−
:** Correspondence on inflated surface using particle system
 
−
:** Tool: ParticleCorrespondence (UNC Slicer3 external module)
 
−
:* '''9. Label map creation'''
 
 
:** Label map creation for cortical thickness computation (WM + GM + "CSF")
 
:** Label map creation for cortical thickness computation (WM + GM + "CSF")
 
:** Tool: ImageMath
 
:** Tool: ImageMath
−
:* '''10. Cortical thickness'''
+
:* '''9. Cortical thickness'''
 
:** Asymmetric local cortical thickness or Laplacian cortical thickness
 
:** Asymmetric local cortical thickness or Laplacian cortical thickness
 
:** Tool: UNCCortThick or measureThicknessFilter (UNC Slicer3 external modules)
 
:** Tool: UNCCortThick or measureThicknessFilter (UNC Slicer3 external modules)
 +
:* '''10. Cortical correspondence'''
 +
:** Correspondence on inflated surface using particle system
 +
:** Tool: ParticleCorrespondence (UNC Slicer3 external module)
 
:* '''11. Group statistical analysis'''
 
:* '''11. Group statistical analysis'''
 
:** Tool: QDEC Slicer module or StatNonParamPDM
 
:** Tool: QDEC Slicer module or StatNonParamPDM

Revision as of 16:51, 9 January 2009

Home < DBP2:UNC:Local Cortical Thickness Pipeline

Back to UNC Cortical Thickness Roadmap


Objective

We would like to create an end-to-end application within Slicer3 allowing individual and group analysis of local cortical thickness.

T1-weighted skull-stripped image
Parcellation image
White matter genus zero surface
Inflated white matter genus zero surface

Pipeline overview

Input: RAW images (T1-weighted, T2-weighted, PD-weighted images)

  • 1. Tissue segmentation
    • Probabilistic atlas-based automatic tissue segmentation via an Expectation-Maximization scheme
    • Tool: itkEMS (UNC Slicer3 external module)
  • 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)
  • 3. White matter map creation
    • Brainstem and cerebellum extraction
    • Adding subcortical structures except amygdala and hippocampus
    • Tool: ImageMath (NITRC module)
  • 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: WMSegPostProcess (Slicer3 external module)
  • 5. Genus zero white matter map image and surface creation
    • Tool: GenusZeroImageFilter (UNC Slicer3 external module)
  • 6. White matter surface inflation
    • Iterative smoothing using relaxation operator (considering average vertex) and L2 norm of the mean curvature as a stopping criterion
    • Iteration stopped if vertices that have too high curvature (some extremities)
    • Tool: MeshInflation (UNC Slicer3 external module)
  • 6 bis(Optional). White matter image fixing if necessary
    • Correction of the white matter map image (corresponding to vertices that have high curvature)
    • Tool: FixImage (UNC Slicer3 external module)
    • Go back to step 5
  • 7. Gray matter map creation
    • Adding genus zero white matter map to gray matter segmentation (without cerebellum and brainstem)
    • Tool: ImageMath
  • 8. Label map creation
    • Label map creation for cortical thickness computation (WM + GM + "CSF")
    • Tool: ImageMath
  • 9. Cortical thickness
    • Asymmetric local cortical thickness or Laplacian cortical thickness
    • Tool: UNCCortThick or measureThicknessFilter (UNC Slicer3 external modules)
  • 10. Cortical correspondence
    • Correspondence on inflated surface using particle system
    • Tool: ParticleCorrespondence (UNC Slicer3 external module)
  • 11. Group statistical analysis
    • Tool: QDEC Slicer module or StatNonParamPDM

Download

Usage

Command line execution

Step by step command line execution

Pipeline validation

Analysis on a small pediatric dataset

Tests will be computed on a small pediatric dataset which includes 2 year-old and 4 year-old cases.

  • 16 autistic cases
  • 1 developmental delay
  • 3 normal control

Comparison to state of the art

We would like to compare our pipeline with FreeSurfer. We will thus perform a regional statistical analysis using Pearson's correlation coefficient on a pediatric dataset including 90 cases.

Two distinct groups are considered: 2 year-old cases and 4 year-old cases.

Planning

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
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