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

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== Objective ==
 
== 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...)
+
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.
 
This page describes the related pipeline with its basic components, as well as its validation.
 +
 +
  
 
== Pipeline overview ==
 
== 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)
  
 
<div style="margin: 20px;">
 
<div style="margin: 20px;">
 
<div style="width: 42%; float: left; padding-right: 3%;">
 
<div style="width: 42%; float: left; padding-right: 3%;">
  
−
Input: RAW images (T1-weighted, T2-weighted, PD-weighted images)
+
Input: CSV file containing RAW images (T1-weighted, T2-weighted, PD-weighted images)
  
 
* '''1. Individual pipeline'''
 
* '''1. Individual pipeline'''
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*** Adding subcortical structures except amygdala and hippocampus
 
*** Adding subcortical structures except amygdala and hippocampus
 
*** Tool: ImageMath (UNC Slicer3 external module)
 
*** Tool: ImageMath (UNC Slicer3 external module)
−
** '''1.4. White matter map post-processing'''
+
** '''1.4. Cortical thickness computation'''
 +
*** Asymmetric cortical thickness
 +
*** Tool: UNCCortThick(UNC Slicer3 external module)
 +
** '''1.5. White matter map post-processing'''
 
*** Largest component computation
 
*** Largest component computation
 
*** Smoothing: Level set smoothing or weighted average filter
 
*** Smoothing: Level set smoothing or weighted average filter
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*** White matter filling
 
*** White matter filling
 
*** Tool: WMSegPostProcess (UNC Slicer3 external module)
 
*** Tool: WMSegPostProcess (UNC Slicer3 external module)
−
** '''1.5. Genus zero white matter map image and surface creation'''
+
** '''1.6. Genus zero white matter map image and surface creation'''
 
*** Tool: GenusZeroImageFilter (UNC Slicer3 external module)
 
*** Tool: GenusZeroImageFilter (UNC Slicer3 external module)
−
** '''1.6. White matter surface inflation'''
+
** '''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
 
*** 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)
 
*** Iteration stopped if vertices that have too high curvature (some extremities)
 
*** Tool: MeshInflation (UNC Slicer3 external module)
 
*** Tool: MeshInflation (UNC Slicer3 external module)
−
** '''1.6 bis(Optional). White matter image fixing if necessary'''
+
** '''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
 
*** Correction of the white matter map image (corresponding to vertices that have high curvature) with connectivity enforcement
 
*** Tool: FixImage (UNC Slicer3 external module)
 
*** Tool: FixImage (UNC Slicer3 external module)
−
*** Go back to step 5
+
*** Go back to step 1.6
−
** '''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'''
 
** '''1.8. Sulcal depth'''
 
*** Sulcal depth computation using genus-zero surface and inflated one
 
*** Sulcal depth computation using genus-zero surface and inflated one
Line 113: Line 117:
 
</div>
 
</div>
  
−
== Download ==
+
== GAMBIT Download ==
 +
 
 +
=== CVS access, Executables and tutorial dataset ===
 +
Available on NITRC : http://www.nitrc.org/projects/gambit/
  
 
=== Brain atlases ===  
 
=== Brain atlases ===  
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=== Done ===
 
=== Done ===
  
−
Steps 1 to 10:
+
* Workflow for group analysis (Slicer3 external module using BatchMake):
−
* Development of UNC Slicer3 modules
+
** Development of UNC Slicer3 modules
−
* Modules applied on small pediatric dataset from the Autism study
+
** Modules applied on small pediatric dataset from the Autism study
−
* Symmetric atlases generation (pediatric, adult, elderly):
+
* Pediatric and adult brain atlases available to the community via MIDAS
 
** T1-weighted atlas
 
** T1-weighted atlas
 
** Tissue segmentation probability maps
 
** Tissue segmentation probability maps
 
** Subcortical structures 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 ===
 
=== In progress ===
  
−
* Step 1.6: Parameter exploration on autism dataset to improve inflation-fixing steps
+
* 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)
 
* 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
+
* 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
−
=== 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 ==
 
== 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.
 
*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.
 +
*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
 
*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.
 
*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
 
*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

Revision as of 19:13, 26 August 2010

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 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

  • 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.
  • 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
  • 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. 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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