DBP2:UNC:Local Cortical Thickness Pipeline

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Cortical thickness on white matter cortical surface

Objective

We would like to create end-to-end applications within Slicer3 allowing individual and group analysis of mesh-based local cortical thickness.


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. Skull stripping using previously computed tissue segmentation label image
      • Tool: SegPostProcess (UNC Slicer3 external module)
    • 2.2. T1-weighted atlas deformable registration
      • B-spline pipeline registration
      • Tool: RegisterImages (Slicer3 module)
    • 2.3. 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 (UNC Slicer3 external module)
  • 4. White matter map post-processing
    • Largest component computation
    • Smoothing: Level set smoothing or weighted average filter
    • Connectivity enforcement (6-connectivity)
    • White matter filling
    • Tool: WMSegPostProcess (UNC 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) with connectivity enforcement
    • 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. Sulcal depth
    • Sulcal depth computation using genus zero surface and inflated one
    • Tool: MeshMath (UNC module)
  • 11. Cortical correspondence
    • Correspondence on inflated surfaces using particle system
    • Tools: ParticleCorrespondencePreProcessing, ParticleCorrespondence, ParticleCorrespondencePostProcessing (UNC Slicer3 external modules)
  • 12. Group statistical analysis
    • Tool: QDEC Slicer module or StatNonParamPDM
T1-weighted image
T1 corrected image
Label image
White matter mesh
T1-weigthed atlas with subcortical structures
ROI segmentation on T1-weigthed stripped image
Genus-zero cortical surface
Inflated cortical surface
Cortical thickness on genus-zero cortical surface
Cortical thickness on inflated genus-zero cortical surface
Sulcal depth on genus-zero cortical surface
Sulcal depth on inflated genus-zero cortical surface
Particles on inflated genus-zero cortical surface

Download

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 an adult dataset (FreeSurfer's publicly available tutorial dataset) including 40 cases.

Planning

Done

Steps 1 to 10:

  • Development of UNC Slicer3 modules (except itkEMS)
  • Modules applied on small pediatric dataset from the Autism study

In progress

  • Step 6: Parameter adjustment on autism dataset to fix bad vertices
  • Step 11: Particle correspondence testing with pediatric surfaces

Future work

  • Full pipeline working on pediatric dataset
  • Workflow for individual analysis as a Slicer3 high-level module using BatchMake
  • Workflow for group analysis

References

  • I. Oguz, M. Niethammer, J. Cates, R. Whitaker, T. Fletcher, C. Vachet, and M. Styner, Cortical Correspondence with Probabilistic Fiber Connectivity, Information Processing in Medical Imaging, IPMI 2009, LNCS, in print.
  • H.C. Hazlett, C. Vachet, C. Mathieu, M. Styner, J. Piven, Use of the Slicer3 Toolkit to Produce Regional Cortical Thickness Measurement of Pediatric MRI Data, presented at the 8th Annual International Meeting for Autism Research (IMFAR) Chicago, IL 2009.
  • C. Mathieu, C. Vachet, H.C. Hazlett, G. Geric, J. Piven, and M. Styner, ARCTIC – Automatic Regional Cortical ThICkness Tool, UNC Radiology Research Day 2009 abstract
  • C. Vachet, H.C. Hazlett, M. Niethammer, I. Oguz, J.Cates, R. Whitaker, J. Piven, M. Styner, Mesh-based Local Cortical Thickness Framework, UNC Radiology Research Day 2009 abstract
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