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

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=== Pipeline description (steps) ===
 
=== Pipeline description (steps) ===
  
Input: T1-weighted image
+
Input: T1-weighted image, T2-weighted image, PD-weighted image
  
 
:* '''1. Tissue segmentation'''
 
:* '''1. Tissue segmentation'''

Revision as of 20:23, 19 August 2008

Home < 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)

Input T1_weighted image: Input T1-weigthed atlas: Input 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 (optionnal)
 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