DBP2:UNC:Local Cortical Thickness Pipeline

Back to UNC Cortical Thickness Roadmap

Objective

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

T1-weighted skull-stripped image

Parcellation image

White matter genus zero surface

Inflated white matter genus zero surface

Sulcal depth on original surface

Sulcal depth on inflated surface

Particles

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 (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

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