DBP2:UNC:Regional Cortical Thickness Pipeline

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Screenshot of the application
Pipeline description

Objective

We would like to create an end-to-end application within Slicer3 allowing individual and group analysis of regional cortical thickness.

General information

Pipeline description (steps)

Input: T1-weighted image, T2-weighted image, PD-weighted image

  • 1. Tissue segmentation
    • Tool: itkEMS (UNC Slicer3 external module)
  • 2. Skull stripping
    • Tool: SegPostProcess (UNC Slicer3 external module)
  • 3. Deformable registration of pediatric regional atlas
    • 3.1 Deformable registration of T1-weighted pediatric atlas
      • Tool: RegisterImages (Slicer3 module)
    • 3.2. Applying transformation to the parcellation map
      • Tool: ResampleVolume2 (Slicer3 module)
  • 4. Cortical Thickness
    • Tool: CortThick (UNC Slicer3 module)

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 'RegionalCortThickPipeline' module, either within Slicer3 or as a command line.

Usage (Command Line)

Inputs: T1-weighted image, T1-weigthed atlas, regional atlas (parcellation map)


A. Pipeline command line

 RegionalCortThickPipeline --T1 Image_T1.gipl --segAtlasDir TissueSegmentationAtlasDirectory/ 
 --atlas Atlas.gipl --atlasParcellation Parcellation.gipl --SaveWM  WMCorticalThicknessMap  --SaveGM GMCorticalThicknessMap


B. Step by step command line

  • 1. Tissue segmentation
    • Input: EMSparam.xml
    • Output: Image_Corrected_EMS.gipl, Label.gipl
 itkEMSCLP --XMLFile EMSparam.xml
  • 2. Skull stripping
    • Input: Label.gipl, Image_Corrected_EMS.gipl
    • Output: Image_Corrected_EMS_Stripped.gipl, BinaryMask.gipl (optional)
 SegPostProcessCLP Label.gipl  Image_Corrected_EMS_Stripped.gipl --skullstripping  Image_Corrected_EMS.gipl
  • 3. Deformable registration of pediatric regional atlas
    • 3.1 Deformable registration of T1-weighted pediatric atlas
      • Input: Atlas.gipl, Image_Corrected_EMS_Stripped.gipl
      • Output: Atlas_Registered.gipl, Atlas_Registered_Transform.txt
  RegisterImages Image_Corrected_EMS_Stripped.gipl Atlas.gipl –resampledImage  Atlas_Registered.gipl –saveTransform Atlas_Registered_Transform.txt –registration PipelineBSpline
    • 3.2. Applying transformation to the parcellation map
      • Input: Parcellation.gipl, Atlas_Registered_Transform.txt, Image_Corrected_EMS_Stripped.gipl
      • Output: Parcellation_Registered.gipl
  ResampleVolume2 Parcellation.gipl Parcellation_Registered.gipl -f Atlas_Registered_Transform.txt -i nn -R Image_Corrected_EMS_Stripped.gipl
  • 4. Cortical Thickness
    • Input: Parcellation_Registered.gipl, Label.gipl
    • Output: CortThick_Result_Dir/, WMCorticalThicknessMap, GMCorticalThicknessMap
  CortThickCLP CortThick_Result_Dir/ --par  Parcellation_Registered.gipl --inputSeg Label.gipl --SaveWM  WMCorticalThicknessMap  --SaveGM GMCorticalThicknessMap

Analysis on a small pediatric dataset

Tests have been computed on a small pediatric dataset, including 2 years old and 4 years old cases

  • 2 Autistic cases
  • 1 developmental delay
  • 1 normal control
T1-weighted skull-stripped image
Parcellation image
Cortical thickness on WM surface
Cortical thickness information

In progress

  • Workflow for individual analysis (Slicer3 external module using BatchMake)

Future work

  • UNC Slicer3 external modules available on NITRIC
  • Pediatric atlas (T1-weighted image and parcellation map) available to the community (XNAT?)
  • Workflow for group analysis