DBP2:UNC:Local Cortical Thickness Pipeline

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Cortical thickness on white matter cortical surface

Objective

We would like to create an end-to-end application within Slicer3 allowing group-wise automatic mesh-based analysis of 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

A Slicer3 high-level module for group-wise cortical thickness analysis has been developed: GAMBIT (Group-wise Automatic Mesh Based analysis of cortIcal Thickness)

Input: CSV file containing 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. Cortical thickness computation
      • Asymmetric cortical thickness
      • Tool: UNCCortThick(UNC Slicer3 external module)
    • 1.5. 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)
    • 1.6. Genus zero white matter map image and surface creation
      • Tool: GenusZeroImageFilter (UNC Slicer3 external module)
    • 1.7. 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)
    • 1.7 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 1.6
    • 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

GAMBIT Download

CVS access, Executables and tutorial dataset

Available on NITRC : http://www.nitrc.org/projects/gambit/

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

  • Workflow for group analysis (Slicer3 external module using BatchMake):
    • Development of UNC Slicer3 modules
    • Modules applied on small pediatric dataset from the Autism study
  • Pediatric and adult brain atlases available to the community via MIDAS
    • T1-weighted atlas
    • Tissue segmentation probability maps
    • Subcortical structures probability maps
    • LBinary mask images
  • GAMBIT available to the community via NITRC: executables (UNC external modules for Slicer3) and tutorial dataset
  • Tutorial with application example on a small dataset
  • GAMBIT source code (CVS) available to the community

In progress

  • Step 1.7: 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)
  • New version of GAMBIT including quality control through MRML scene, and WM, GM models generation
  • GAMBIT executable can be downloaded directly within Slicer3 using the extension wizard

References

  • 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 2010 abstract
  • 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, 21:651-63
  • 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
  • Oguz, I., Cates, J., Fletcher, T., Whitaker, R., Cool, D., Aylward, S., Styner, M., Cortical correspondence using entropy-based particle systems and local features, IEEE Symposium on Biomedical Imaging ISBI 2008. 1637– 1640
  • J. Cates, P. Fletcher, M. Styner, H. Hazlett, R. Whitaker, Particle-based shape analysis of multi-object complexes, MICCAI 2008, 477-85
  • Cates, J., Fletcher, P., Whitaker, R.: Entropy-based particle systems for shape correspondence. MFCA Workshop, MICCAI 2006, 90–99
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