DBP2:UNC:Regional Cortical Thickness Pipeline

Back to UNC Cortical Thickness Roadmap

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

Main pipeline description (steps)

Input: RAW images (T1-weighted, T2-weighted, PD-weighted images)

• 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

• 16 Autistic cases
• 1 developmental delay
• 3 normal control

We would like to perform a regional analysis (Pearson correlation analysis) to compare our method with FreeSurfer.

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)
• Workflow for group analysis (KWWidgets application using BatchMake)
• Regional analysis to compare our method with results from FreeSurfer
• Tutorials

Future work

• UNC Slicer3 external modules available on NITRIC
• Pediatric atlases (T1-weighted image, parcellation map, probability maps) available to the community (NITRIC or PubDB?)