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

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:** Tool: CortThick (UNC Slicer3 external module)
 
:** Tool: CortThick (UNC Slicer3 external module)
 
:* '''4. Statistics'''
 
:* '''4. Statistics'''
:** Generate spreesheats with volume informations
+
:** Volume information (WM, GM, CSF, lobes) stored in spreadsheet
 
:** Tools: ImageMath, ImageStat (UNC Slicer3 external modules)
 
:** Tools: ImageMath, ImageStat (UNC Slicer3 external modules)
:* '''5. Mesh Creation'''
+
:* '''5. Mesh creation'''
:** Generation of white matter and grey matter meshes
+
:** White matter and grey matter meshes creation
 
:** Tool: ModelMaker (Slicer3 module)
 
:** Tool: ModelMaker (Slicer3 module)
:* '''6. MRML scene creation'''
+
:* '''6. MRML scene creation for quality control'''
:** Creation of a MRML scene describing all the steps of the pipeline
+
:** MRML scene creation allowing quality control for each step of the pipeline
 
+
:** Output images and surfaces are automatically displayed
  
 
All the tools used in the current pipeline are Slicer3 modules, some of them being UNC external modules.
 
All the tools used in the current pipeline are Slicer3 modules, some of them being UNC external modules.

Revision as of 15:33, 29 April 2009

Home < DBP2:UNC:Regional Cortical Thickness Pipeline

Back to UNC Cortical Thickness Roadmap

ARCTIC Logo


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 developed: 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 external module)
  • 4. Statistics
    • Volume information (WM, GM, CSF, lobes) stored in spreadsheet
    • Tools: ImageMath, ImageStat (UNC Slicer3 external modules)
  • 5. Mesh creation
    • White matter and grey matter meshes creation
    • Tool: ModelMaker (Slicer3 module)
  • 6. MRML scene creation for quality control
    • MRML scene creation allowing quality control for each step of the pipeline
    • Output images and surfaces are automatically displayed

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.

ARCTIC Download

CVS access, Executables and tutorial dataset

Available on NITRC : http://www.nitrc.org/projects/arctic/

Complementary Downloads

Pediatric atlas

Available on MIDAS : 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


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"
  • Pediatric atlases available to the community through MIDAS
  • ARCTIC available to the community through NITRC: executables (UNC external modules for Slicer3) and Tutorial dataset
  • New version including quality control through MRML scene, and WM, GM models generation
  • ARCTIC source code (SVN, CVS) available to the community

In progress

  • Comparison to FreeSurfer (180 cases dataset): pearson correlation analysis
  • Mac and windows executables available on to the community through NITRC
  • Input images orientation problem automatically managed by ARCTIC