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. Intensity inhomogeneity correction
    • N4 Bias field correction
    • Tool: N4ITKBiasFieldCorrection (Slicer3 module)
  • 1.2. Tissue segmentation
    • Probabilistic atlas-based automatic tissue segmentation via an Expectation-Maximization scheme
    • Tool: itkEMS (UNC Slicer3 external module)
  • 1.3. Atlas-based ROI segmentation: subcortical structures, lateral ventricles, parcellation
    • 1.3.1. Skull stripping using previously computed tissue segmentation label image
      • Tool: SegPostProcess (UNC Slicer3 external module)
    • 1.3.2. T1-weighted atlas registration
      • B-spline based or demons deformable atlas registration
      • Tool: BRAINSFit, BRAINSDemonWarp,BRAINSResample (Slicer3 modules)
    • 1.23.3. Applying transformations to the structures
      • Tool: ResampleVolume2 (Slicer3 module)
  • 1.4. White matter map creation
    • Brainstem and cerebellum extraction
    • Adding subcortical structures except amygdala and hippocampus
    • Tool: ImageMath (UNC Slicer3 external module)
  • 1.5. Cortical thickness computation
    • Asymmetric cortical thickness on white matter map
    • Tool: UNCCortThick (UNC Slicer3 external module)
  • 1.6. 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)
  • 1.7. 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
  • 1.9. Sulcal depth
    • Sulcal depth computation using genus-zero surface and inflated one
    • Tool: MeshMath (UNC module)
  • 1.10. Surface area computation
    • Lobar surface area measurement on smoothed genus-zero surface
    • Tool: MeshMath (UNC module)
  • 1.11. 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 of folded and inflated meshes 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

All the tools used in the current pipeline are Slicer3 modules, some of them being UNC external modules. The user can thus compute an individual regional cortical thickness analysis by running the 'GAMBIT' module, either within Slicer3 or as a command line.

GAMBIT download

Source code, executables and tutorial are available on NITRC

Complementary downloads

Brain atlases

Four brain atlases are available on MIDAS and on NITRC:

• Pediatric atlas
• Adult atlas
• Elderly atlas
• Primate atlas

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.

Tutorials

• GAMBIT tutorial : end-to-end Slicer3 module to perform group-wise automatic mesh-based analysis of cortical thickness ‏ [ppt]‏ [pdf]

Cortical thickness overlayed on inflated cortical surfaces

Sucal depth overlayed on inflated cortical surfaces

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 state of the art, such as FreeSurfer.

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