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.

This page describes the related pipeline with its basic components, as well as its validation.

Pipeline overview

A Slicer3 high-level module for individual cortical thickness analysis has been developped: ARCTIC (Automatic Regional Cortical ThICkness)

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

• 1. Tissue segmentation
  • Probabilistic atlas-based automatic tissue segmentation via an Expectation-Maximization scheme
  • Tool: itkEMS (UNC Slicer3 external module)
• 2. Regional atlas deformable registration
  • 2.1 Skull stripping using previously computed tissue segmentation label image
    • Tool: SegPostProcess (UNC Slicer3 external module)
  • 2.2 T1-weighted atlas deformable registration
    • B-spline pipeline registration
    • Tool: RegisterImages (Slicer3 module)
  • 2.3. Applying transformation to the parcellation map
    • Tool: ResampleVolume2 (Slicer3 module)
• 3. Cortical Thickness
  • Sparse asymmetric local 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 'ARCTIC' module, either within Slicer3 or as a command line.

Download

Executable and tutorial dataset: http://www.nitrc.org/projects/arctic/

Pediatric atlas: http://www.insight-journal.org/midas/item/view/2277

Tutorials

• ARCTIC tutorial : end-to-end Slicer3 module to perform automatic regional cortical thickness analysis‏ [ppt]‏ [pdf]

• UNC Modules tutorial : UNC Slicer3 modules to perform regional cortical thickness analysis step by step [ppt]‏ [pdf]‏

T1-weighted skull-stripped image

Parcellation image

Cortical thickness on WM surface

Cortical thickness information

ARCTIC (Automatic Regional Cortical ThICkness) usage:

Prerequities

Add directories to the PATH variable

 -tcsh usage : setenv PATH ARCTIC-Executables-Directory:Slicer3D-Plugins-Directory:Batchmake-Directory:${PATH}
 -bash usage : export PATH=ARCTIC-Executables-Directory:Slicer3D-Plugins-Directory:Batchmake-Directory:${PATH}
 Notice : To execute ARCTIC within Slicer3D, it is not necessary to add "Slicer3D-Plugins-Directory" in the PATH.

Set an environment variable

 -tcsh usage : setenv BatchmakeWrapper_Dir Batchmake-Wrapper-Directory
 -bash usage : export BatchmakeWrapper_Dir=Batchmake-Wrapper-Directory

Set ARCTIC as a Slicer3D module

 Within Slicer3D : View -> Applications Settings -> Module Settings -> Add a preset button and then select the ARCTIC-Executables-Directory/

Command line execution

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

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

Step by step command line execution

• 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

Pipeline validation

Analysis on a small pediatric dataset

Tests have been 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 FreeSurfer. We have thus started to perform a regional statistical analysis using Pearson's correlation coefficient on a pediatric dataset including 90 cases.

Two distinct groups are considered: 2 year-old cases and 4 year-old cases.

Planning

Done

• Workflow for individual analysis (Slicer3 external module using BatchMake)
• 2 Tutorials with application example on a small dataset : "How to use the UNC modules to compute the regional cortical thickness" and "How to use ARCTIC"

In progress

• Pediatric atlases available to the community through MIDAS
• Comparison to FreeSurfer: pearson correlation analysis
• ARCTIC available to the community through NITRC: executables (UNC external modules for Slicer3), source code (SVN) and Tutorial dataset

Future work

• Workflow for group analysis (KWWidgets application using BatchMake)