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 as well as other surface measurements (surface area...)

This page describes the related pipeline with its basic components, as well as its validation.

Pipeline overview

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

  • 1. Individual pipeline
    • 1.1. Tissue segmentation
      • Probabilistic atlas-based automatic tissue segmentation via an Expectation-Maximization scheme
      • Tool: itkEMS (UNC Slicer3 external module)
    • 1.2. Atlas-based ROI segmentation: subcortical structures, lateral ventricles, parcellation
      • 1.2.1. Skull stripping using previously computed tissue segmentation label image
        • Tool: SegPostProcess (UNC Slicer3 external module)
      • 1.2.2. T1-weighted atlas deformable registration
        • B-spline pipeline registration
        • Tool: RegisterImages (Slicer3 module)
      • 1.2.3. Applying transformations to the structures
        • Tool: ResampleVolume2 (Slicer3 module)
    • 1.3. White matter map creation
      • Brainstem and cerebellum extraction
      • Adding subcortical structures except amygdala and hippocampus
      • Tool: ImageMath (UNC Slicer3 external module)
    • 1.4. 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)
    • 1.5. Genus zero white matter map image and surface creation
      • Tool: GenusZeroImageFilter (UNC Slicer3 external module)
    • 1.6. 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)
    • 1.6 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
    • 1.7. Cortical thickness computation
      • Asymmetric local cortical thickness or Laplacian cortical thickness
      • Tool: UNCCortThick or measureThicknessFilter (UNC Slicer3 external modules)
    • 1.8. Sulcal depth
      • Sulcal depth computation using genus-zero surface and inflated one
      • Tool: MeshMath (UNC module)
    • 1.9. Surface area computation
      • Lobar surface area measurement on smoothed genus-zero surface
      • Tool: MeshMath (UNC module)
    • 1.10. Particles initialization for cortical correspondence
      • Initializing particles on inflated genus-zero surface using 98-lobe parcellation map and genus zero surface
      • Tool: ParticleInitializer (UNC Slicer3 external module)
  • 2. Particle-based shape correspondence
    • Correspondence on inflated surfaces using particle system
    • 2.1. Preprocessing
      • Distance maps creation from inflated genus-zero surfaces with slight gaussian blurring
      • Tool: ParticleCorrespondencePreProcessing (UNC Slicer3 external module)
    • 2.2. Correspondence optimization
      • Particle-based shape correspondence optimization (using sulcal depth) with Procrustes alignement
      • Tool: ShapeWorksRun (Utah Slicer3 external module)
    • 2.3. Postprocessing
      • Re-meshing using template
      • Tool: ParticleCorrespondencePostProcessing (UNC Slicer3 external module)
    • 2.4. Cortical thickness interpolation
      • Cortical thickness interpolation on surface in correspondence
      • Tool: MeshMath (UNC module)
  • 3. 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

Brain atlases

Four brain atlases are available on MIDAS and on NITRC:

Pediatric MRI Brain data

Data of 2 autistic children and 2 normal controls (male, female) scanned at 2 years with follow up at 4 years from a 1.5T Siemens scanner. Files include structural data, tissue segmentation label map and subcortical structures segmentation.

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
  • Modules applied on small pediatric dataset from the Autism study
  • Symmetric atlases generation (pediatric, adult, elderly):
    • T1-weighted atlas
    • Tissue segmentation probability maps
    • Subcortical structures probability maps

In progress

  • Step 1.6: Parameter exploration on autism dataset to improve inflation-fixing steps
  • Step 2: Particle correspondence testing with pediatric surfaces (Meeting with Josh Cates at UNC - February 2010)
  • Automatization of several steps using ShapeWorksRun and parameter files

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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