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

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Input: RAW images (T1-weighted, T2-weighted, PD-weighted images)
 
Input: RAW images (T1-weighted, T2-weighted, PD-weighted images)
  
−
* '''1. Tissue segmentation'''
+
* '''1. Individual pipeline'''
−
** Probabilistic atlas-based automatic tissue segmentation via an Expectation-Maximization scheme
+
** '''1.1. Tissue segmentation'''
−
** Tool: itkEMS (UNC Slicer3 external module)
+
*** Probabilistic atlas-based automatic tissue segmentation via an Expectation-Maximization scheme
−
* '''2. Atlas-based ROI segmentation:''' subcortical structures, lateral ventricles, parcellation
+
*** Tool: itkEMS (UNC Slicer3 external module)
−
** 2.1. Skull stripping using previously computed tissue segmentation label image
+
** '''1.2. Atlas-based ROI segmentation:''' subcortical structures, lateral ventricles, parcellation
−
*** Tool: SegPostProcess (UNC Slicer3 external module)  
+
*** 1.2.1. Skull stripping using previously computed tissue segmentation label image
−
** 2.2. T1-weighted atlas deformable registration
+
**** Tool: SegPostProcess (UNC Slicer3 external module)  
−
*** B-spline pipeline registration
+
*** 1.2.2. T1-weighted atlas deformable registration
−
*** Tool: RegisterImages (Slicer3 module)
+
**** B-spline pipeline registration
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** 2.3. Applying transformations to the structures
+
**** Tool: RegisterImages (Slicer3 module)
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*** Tool: ResampleVolume2 (Slicer3 module)
+
*** 1.2.3. Applying transformations to the structures
−
* '''3. White matter map creation'''
+
**** Tool: ResampleVolume2 (Slicer3 module)
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** Brainstem and cerebellum extraction
+
** '''1.3. White matter map creation'''
−
** Adding subcortical structures except amygdala and hippocampus
+
*** Brainstem and cerebellum extraction
−
** Tool: ImageMath (UNC Slicer3 external module)
+
*** Adding subcortical structures except amygdala and hippocampus
−
* '''4. White matter map post-processing'''
+
*** Tool: ImageMath (UNC Slicer3 external module)
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** Largest component computation
+
** '''1.4. White matter map post-processing'''
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** Smoothing: Level set smoothing or weighted average filter
+
*** Largest component computation
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** Connectivity enforcement (6-connectivity)
+
*** Smoothing: Level set smoothing or weighted average filter
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** White matter filling
+
*** Connectivity enforcement (6-connectivity)
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** Tool: WMSegPostProcess (UNC Slicer3 external module)
+
*** White matter filling
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* '''5. Genus zero white matter map image and surface creation'''
+
*** Tool: WMSegPostProcess (UNC Slicer3 external module)
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** Tool: GenusZeroImageFilter (UNC Slicer3 external module)
+
** '''1.5. Genus zero white matter map image and surface creation'''
−
* '''6. White matter surface inflation'''
+
*** Tool: GenusZeroImageFilter (UNC Slicer3 external module)
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** Iterative smoothing using relaxation operator (considering average vertex) and L2 norm of the mean curvature as a stopping criterion
+
** '''1.6. White matter surface inflation'''
−
** Iteration stopped if vertices that have too high curvature (some extremities)
+
*** Iterative smoothing using relaxation operator (considering average vertex) and L2 norm of the mean curvature as a stopping criterion
−
** Tool: MeshInflation (UNC Slicer3 external module)
+
*** Iteration stopped if vertices that have too high curvature (some extremities)
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* '''6 bis(Optional). White matter image fixing if necessary'''
+
*** Tool: MeshInflation (UNC Slicer3 external module)
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** Correction of the white matter map image (corresponding to vertices that have high curvature) with connectivity enforcement
+
** '''1.6 bis(Optional). White matter image fixing if necessary'''
−
** Tool: FixImage (UNC Slicer3 external module)
+
*** Correction of the white matter map image (corresponding to vertices that have high curvature) with connectivity enforcement
−
** Go back to step 5
+
*** Tool: FixImage (UNC Slicer3 external module)
−
* '''7. Gray matter map creation'''
+
*** Go back to step 5
−
** Adding genus zero white matter map to gray matter segmentation (without cerebellum and brainstem)
+
** '''1.7. Gray matter map creation'''
−
** Tool: ImageMath
+
*** Adding genus zero white matter map to gray matter segmentation (without cerebellum and brainstem)
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* '''8. Label map creation'''
+
*** Tool: ImageMath
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** Label map creation for cortical thickness computation (WM + GM + CSF)
+
** '''1.8. Label map creation'''
−
** Tool: ImageMath
+
*** Label map creation for cortical thickness computation (WM + GM + CSF)
−
* '''9. Cortical thickness'''
+
*** Tool: ImageMath
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** Asymmetric local cortical thickness or Laplacian cortical thickness
+
** '''1.9. Cortical thickness'''
−
** Tool: UNCCortThick or measureThicknessFilter (UNC Slicer3 external modules)
+
*** Asymmetric local cortical thickness or Laplacian cortical thickness
−
* '''10. Sulcal depth'''
+
*** Tool: UNCCortThick or measureThicknessFilter (UNC Slicer3 external modules)
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** Sulcal depth computation using genus zero surface and inflated one
+
** '''1.10. Sulcal depth'''
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** Tool: MeshMath (UNC module)
+
*** Sulcal depth computation using genus zero surface and inflated one
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* '''11. Particles initialization for cortical correspondence'''
+
*** Tool: MeshMath (UNC module)
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** Initializing particles on inflated surface using parcellation map and genus zero surface
+
** '''1.11. Particles initialization for cortical correspondence'''
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** Tools: ParticleInitializer (UNC Slicer3 external modules)
+
*** Initializing particles on inflated surface using parcellation map and genus zero surface
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* '''12. Cortical correspondence'''
+
*** Tools: ParticleInitializer (UNC Slicer3 external modules)
 +
* '''1.2. Cortical correspondence'''
 
** Correspondence on inflated surfaces using particle system
 
** Correspondence on inflated surfaces using particle system
 
** Tools: ParticleCorrespondencePreProcessing, ParticleCorrespondence, ParticleCorrespondencePostProcessing (UNC Slicer3 external modules)
 
** Tools: ParticleCorrespondencePreProcessing, ParticleCorrespondence, ParticleCorrespondencePostProcessing (UNC Slicer3 external modules)
−
* '''13. Group statistical analysis'''
+
* '''1.3. Group statistical analysis'''
 
** Tool: QDEC Slicer module or StatNonParamPDM
 
** Tool: QDEC Slicer module or StatNonParamPDM
  

Revision as of 21:15, 15 December 2009

Home < DBP2:UNC:Local Cortical Thickness Pipeline

Back to UNC Cortical Thickness Roadmap

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. 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. Gray matter map creation
      • Adding genus zero white matter map to gray matter segmentation (without cerebellum and brainstem)
      • Tool: ImageMath
    • 1.8. Label map creation
      • Label map creation for cortical thickness computation (WM + GM + CSF)
      • Tool: ImageMath
    • 1.9. Cortical thickness
      • Asymmetric local cortical thickness or Laplacian cortical thickness
      • Tool: UNCCortThick or measureThicknessFilter (UNC Slicer3 external modules)
    • 1.10. Sulcal depth
      • Sulcal depth computation using genus zero surface and inflated one
      • Tool: MeshMath (UNC module)
    • 1.11. Particles initialization for cortical correspondence
      • Initializing particles on inflated surface using parcellation map and genus zero surface
      • Tools: ParticleInitializer (UNC Slicer3 external modules)
  • 1.2. Cortical correspondence
    • Correspondence on inflated surfaces using particle system
    • Tools: ParticleCorrespondencePreProcessing, ParticleCorrespondence, ParticleCorrespondencePostProcessing (UNC Slicer3 external modules)
  • 1.3. 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:

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 initialization
  • Step 12: Particle correspondence testing with pediatric surfaces
  • Automatization of several steps
  • Symmetric atlases generation (pediatric, adult, elderly):
    • T1-weighted atlas
    • Tissue segmentation probability maps
    • Subcortical structures probability maps

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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