DBP2:UNC:Regional Cortical Thickness Pipeline

Back to UNC Cortical Thickness Roadmap

T1-weighted skull-stripped image

Parcellation image

Cortical thickness on WM surface

Cortical thickness information

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 a regional cortical thickness analysis on one data, either within Slicer3 or by running the tools as command lines.

Usage (Command Line)

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

• 1. Tissue segmentation
  • Input: EMSparam.xml
  • Output: Image_Corrected_EMS.gipl, Label.gipl

 itkEMSCLP 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 (--mask BinaryMask.gipl –dilate)

• 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/

  CortThickCLP CortThick_Result_Dir/ --par  Parcellation_Registered.gipl --inputSeg Label.gipl --Wm --Gm --Vtk --Sdm –BvsI --GMMaps

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

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