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

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Back to [[DBP2:UNC:Cortical_Thickness_Roadmap | UNC Cortical Thickness Roadmap]]
 
Back to [[DBP2:UNC:Cortical_Thickness_Roadmap | UNC Cortical Thickness Roadmap]]
  
 +
[[Image:MeshBasedCortThick_CorticalThickness.jpg|thumb|250px|Cortical thickness on white matter cortical surface]]
  
 
== Objective ==
 
== Objective ==
  
 
We would like to create end-to-end applications within Slicer3 allowing individual and group analysis of mesh-based local cortical thickness.
 
We would like to create end-to-end applications within Slicer3 allowing individual and group analysis of mesh-based local cortical thickness.
 +
 +
 +
== Pipeline overview ==
 +
 +
<div style="margin: 20px;">
 +
<div style="width: 42%; float: left; padding-right: 3%;">
 +
 +
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. Skull stripping using previously computed tissue segmentation label image
 +
*** Tool: SegPostProcess (UNC Slicer3 external module)
 +
** 2.2. T1-weighted atlas deformable registration
 +
*** B-spline pipeline registration
 +
*** Tool: RegisterImages (Slicer3 module)
 +
** 2.3. 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
 +
 +
</div>
 +
<div style="width: 55%; float: left;">
  
 
<center>
 
<center>
 
{|
 
{|
−
|[[Image:T1Image.jpg|thumb|250px|T1-weighted skull-stripped image]]
+
| [[Image:MeshBasedCortThick_T1Image.jpg|thumb|175px|T1-weighted image]]
−
|valign="center"|[[Image:Parcellation.jpg|thumb|250px|Parcellation image]]
+
| [[Image:MeshBasedCortThick_T1CorrectedImage.jpg|thumb|175px|T1 corrected image]]
−
|valign="center"|[[Image:WMMesh.jpg|thumb|250px|White matter genus zero surface]]
+
| [[Image:MeshBasedCortThick_T1LabelImage.jpg|thumb|175px|Label image]]
−
|valign="center"|[[Image:WMMeshInflated.jpg|thumb|250px|Inflated white matter genus zero surface]]
+
| [[Image:MeshBasedCortThick_T1WMMesh.jpg|thumb|175px|White matter mesh]]
 
|}
 
|}
−
</center>
 
  
−
<center>
 
 
{|
 
{|
−
|[[Image:SulcalDepth1.jpg|thumb|250px|Sulcal depth on original surface]]
+
| [[Image:MeshBasedCortThick_BrainROIAtlas_AllROIMesh.jpg|thumb|200px|T1-weigthed atlas with subcortical structures]]
−
|valign="center"|[[Image:SulcalDepth2.jpg|thumb|250px|Sulcal depth on inflated surface]]
+
| [[Image:MeshBasedCortThick_T1StrippedAllROIMesh.jpg|thumb|200px|ROI segmentation on T1-weigthed stripped image]]
−
|valign="center"|[[Image:Particles.jpg|thumb|250px|Particles]]
+
|}
 +
{|
 +
| [[Image:MeshBasedCortThick_WMMesh.jpg|thumb|200px|Genus-zero cortical surface]]
 +
| [[Image:MeshBasedCortThick_WMMeshInflated.jpg|thumb|200px|Inflated cortical surface]]
 +
|}
 +
{|
 +
| [[Image:MeshBasedCortThick_CorticalThickness.jpg|thumb|200px|Cortical thickness on genus-zero cortical surface]]
 +
| [[Image:MeshBasedCortThick_CorticalThicknessInflated.jpg|thumb|200px|Cortical thickness on inflated genus-zero cortical surface]]
 +
|}
 +
{|
 +
| [[Image:MeshBasedCortThick_SulcalDepth.jpg|thumb|200px|Sulcal depth on genus-zero cortical surface]]
 +
| [[Image:MeshBasedCortThick_SulcalDepthInflated.jpg|thumb|200px|Sulcal depth on inflated genus-zero cortical surface]]
 +
| [[Image:MeshBasedCortThick_Particles.jpg|thumb|200px|Particles on inflated genus-zero cortical surface]]
 
|}
 
|}
 
</center>
 
</center>
−
 
+
</div>
−
== Pipeline overview ==
+
</div>
−
 
 
−
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
 
  
 
== Download ==
 
== Download ==
Line 81: Line 103:
  
 
Tests will be computed on a small pediatric dataset which includes 2 year-old and 4 year-old cases.
 
Tests will be computed on a small pediatric dataset which includes 2 year-old and 4 year-old cases.
−
:* 16 autistic cases  
+
* 16 autistic cases  
−
:* 1 developmental delay
+
* 1 developmental delay
−
:* 3 normal control
+
* 3 normal control
  
 
=== Comparison to state of the art ===
 
=== Comparison to state of the art ===
Line 94: Line 116:
  
 
Steps 1 to 10:
 
Steps 1 to 10:
−
:* Development of UNC Slicer3 modules (except itkEMS)
+
* Development of UNC Slicer3 modules (except itkEMS)
−
:* Modules applied on small pediatric dataset from the Autism study
+
* Modules applied on small pediatric dataset from the Autism study
  
 
=== In progress ===
 
=== In progress ===
  
−
:* Step 6: Parameter adjustment on autism dataset to fix bad vertices
+
* Step 6: Parameter adjustment on autism dataset to fix bad vertices
−
:* Step 11: Particle correspondence testing with pediatric surfaces
+
* Step 11: Particle correspondence testing with pediatric surfaces
  
 
=== Future work ===
 
=== Future work ===
  
−
:* Full pipeline working on pediatric dataset
+
* Full pipeline working on pediatric dataset
−
:* Workflow for individual analysis as a Slicer3 high-level module using BatchMake
+
* Workflow for individual analysis as a Slicer3 high-level module using BatchMake
−
:* Workflow for group analysis
+
* Workflow for group analysis
  
 
== References ==
 
== References ==

Revision as of 21:15, 4 June 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. 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. Skull stripping using previously computed tissue segmentation label image
      • Tool: SegPostProcess (UNC Slicer3 external module)
    • 2.2. T1-weighted atlas deformable registration
      • B-spline pipeline registration
      • Tool: RegisterImages (Slicer3 module)
    • 2.3. 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
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

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
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