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

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Back to [[DBP2:UNC:Cortical_Thickness_Roadmap | UNC Cortical Thickness Roadmap]]
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Back to [[DBP2:UNC:Cortical_Thickness_Roadmap | UNC Cortical Thickness Roadmap]]
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__NOTOC__
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[[Image:ArcticLogo.png|thumb|400px|ARCTIC Logo|right]]
  
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<center>
 
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{|
 
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|[[Image:Screenshot_Slicer.jpg|thumb|250px|Screenshot of the application]]
 
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|valign="center"|[[Image:Schema_pipeline.jpg|thumb|600px|Pipeline description]]
 
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|}
 
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</center>
 
  
 
== Objective ==
 
== Objective ==
  
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We would like to create an end-to-end application within Slicer3 allowing individual and group analysis of regional cortical thickness : ARCTIC (Automatic Regional Cortical ThICkness)
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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.
 +
 
  
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== General information ==
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== Pipeline overview ==
  
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=== Main pipeline description (steps) ===
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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)
 
Input: RAW images (T1-weighted, T2-weighted, PD-weighted images)
  
 
:* '''1. Tissue segmentation'''
 
:* '''1. Tissue segmentation'''
 +
:** Probabilistic atlas-based automatic tissue segmentation via an Expectation-Maximization scheme
 
:** Tool: itkEMS (UNC Slicer3 external module)
 
:** Tool: itkEMS (UNC Slicer3 external module)
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:* '''2. Skull stripping'''
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:* '''2. Regional atlas deformable registration'''
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:** Tool: SegPostProcess (UNC Slicer3 external module)
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:** 2.1 Skull stripping using previously computed tissue segmentation label image
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:* '''3. Deformable registration of pediatric regional atlas'''
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:*** Tool: SegPostProcess (UNC Slicer3 external module)  
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:** 3.1 Deformable registration of T1-weighted pediatric atlas
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:** 2.2 T1-weighted atlas deformable registration
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:***Tool: RegisterImages (Slicer3 module)  
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:*** B-spline pipeline registration
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:** 3.2. Applying transformation to the parcellation map
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:*** Tool: RegisterImages (Slicer3 module)  
 +
:** 2.3. Applying transformation to the parcellation map
 
:*** Tool: ResampleVolume2 (Slicer3 module)
 
:*** Tool: ResampleVolume2 (Slicer3 module)
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:* '''4. Cortical Thickness'''
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:* '''3. Cortical thickness measurement'''
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:** Tool: CortThick (UNC Slicer3 module)
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:** 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.
 
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.
+
The user can thus compute an individual regional cortical thickness analysis by running the 'ARCTIC' module, either within Slicer3 or as a command line.
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−
=== Usage (Command Line) ===
 
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Inputs: T1-weighted image, T1-weigthed atlas, regional atlas (parcellation map)
 
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'''0. Before using the pipeline'''
 
  
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Add the executables in the PATH.
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== Download ==
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  -tcsh usage : setenv PATH ARCTIC-Executables-Directory/:Slicer3D-Plugins-Directory/:Batchmake-Drectory/:${PATH}
 
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  -bash usage : export PATH=ARCTIC-Executables-Directory/:Slicer3D-Plugins-Directory/:Batchmake-Drectory/:${PATH}
 
  
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Set the pipeline environment variable
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=== ARCTIC download ===
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  -tcsh usage : setenv BatchmakeWrapper_Dir Batchmake-Wrapper-Directory/
 
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  -bash usage : export BatchmakeWrapper_Dir=Batchmake-Wrapper-Directory/
 
  
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Set the pipeline as a Slicer3D module
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Source code, executables and tutorial are available on [http://www.nitrc.org/projects/arctic NITRC]
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  Within Slicer3D : View->Applications Settings->Add a preset and then select the ARCTIC-Executables-Directory/
 
  
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'''A. Pipeline command line'''
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=== Complementary downloads ===
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  RegionalCortThickPipeline --T1 Image_T1.gipl --segAtlasDir TissueSegmentationAtlasDirectory/
 
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  --atlas Atlas.gipl --atlasParcellation Parcellation.gipl --SaveWM  WMCorticalThicknessMap  --SaveGM GMCorticalThicknessMap
 
  
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'''B. Step by step command line'''
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==== Brain atlases ====
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:* '''1. Tissue segmentation'''
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Four brain atlases are available on MIDAS:
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:**Input: EMSparam.xml
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:* [http://www.insight-journal.org/midas/item/view/2277 Pediatric atlas]
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:**Output: Image_Corrected_EMS.gipl, Label.gipl
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:* [http://www.insight-journal.org/midas/item/view/2328 Adult atlas]
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  itkEMSCLP --XMLFile EMSparam.xml
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:* [http://www.insight-journal.org/midas/item/view/2330 Elderly atlas]
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:* '''2. Skull stripping'''
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:* [http://www.insight-journal.org/midas/item/view/2283 Primate atlas]
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:**Input: Label.gipl,  Image_Corrected_EMS.gipl
 
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:**Output: Image_Corrected_EMS_Stripped.gipl, BinaryMask.gipl (optional)
 
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  SegPostProcessCLP Label.gipl  Image_Corrected_EMS_Stripped.gipl --skullstripping  Image_Corrected_EMS.gipl
 
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:* '''3. Deformable registration of pediatric regional atlas'''
 
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:** 3.1 Deformable registration of T1-weighted pediatric atlas
 
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:***Input: Atlas.gipl, Image_Corrected_EMS_Stripped.gipl
 
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:***Output: Atlas_Registered.gipl, Atlas_Registered_Transform.txt
 
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  RegisterImages Image_Corrected_EMS_Stripped.gipl Atlas.gipl –resampledImage  Atlas_Registered.gipl –saveTransform Atlas_Registered_Transform.txt –registration PipelineBSpline
 
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:** 3.2. Applying transformation to the parcellation map
 
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:***Input: Parcellation.gipl,  Atlas_Registered_Transform.txt, Image_Corrected_EMS_Stripped.gipl
 
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:***Output:  Parcellation_Registered.gipl
 
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  ResampleVolume2 Parcellation.gipl Parcellation_Registered.gipl -f Atlas_Registered_Transform.txt -i nn -R Image_Corrected_EMS_Stripped.gipl
 
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:* '''4. Cortical Thickness'''
 
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:**Input: Parcellation_Registered.gipl, Label.gipl
 
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:**Output: CortThick_Result_Dir/, WMCorticalThicknessMap, GMCorticalThicknessMap
 
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  CortThickCLP CortThick_Result_Dir/ --par  Parcellation_Registered.gipl --inputSeg Label.gipl --SaveWM  WMCorticalThicknessMap  --SaveGM GMCorticalThicknessMap
 
  
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=== Analysis on a small pediatric dataset ===
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==== Pediatric Brain MRI data ====  
 +
[http://insight-journal.org/midas/community/view/24 Data of 2 autistic children and 2 normal controls] (male, female) scanned at 2 years with follow up at 4 years from a 1.5T Siemens scanner. Files include structural data, tissue segmentation label map and subcortical structures segmentation.
  
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Tests have been computed on a small pediatric dataset, including 2 years old and 4 years old cases
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==== Tutorials ====
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:* 16 Autistic cases
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*'''ARCTIC tutorial''' : end-to-end Slicer3 module to perform automatic regional cortical thickness analysis[[Media:ARCTIC-Slicer3-Tutorial.ppt|‏ [ppt]]][[Media:ARCTIC-Slicer3-Tutorial.pdf|‏ [pdf]]]
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:* 1 developmental delay
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**1st Prize: NAMIC tutorial contest AHM 2009
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:* 3 normal control
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**2nd Prize: NAMIC tutorial contest summer project week 2009
 +
*'''UNC Modules tutorial''' : UNC Slicer3 modules to perform regional cortical thickness analysis step by step[[Media:UNC_Modules-Slicer3-Tutorial.ppt| [ppt]‏]][[Media:UNC_Modules-Slicer3-Tutorial.pdf| [pdf]‏]]
  
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We would like to perform a regional analysis (Pearson correlation analysis) to compare our method with FreeSurfer.
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*'''[https://www.slicer.org/wiki/Modules:ARCTIC-Documentation-3.6 Online documentation within Slicer 3.6]'''
  
 
<center>
 
<center>
Line 96: Line 79:
 
</center>
 
</center>
  
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=== In progress ===
+
== 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 performed a [[DBP2:UNC:Cortical_Thickness_Comparison | regional statistical analysis]] based on Pearson's correlation on an adult dataset (FreeSurfer's publicly available dataset) including 40 cases. We also performed [[DBP2:UNC:Cortical_Thickness_Comparison#Tests_on_a_longitudinal_pediatric_autism_study | tests on a longitudinal autism study]] of 86 subject aged 2-4 years.
 +
 
 +
== Planning ==
 +
 
 +
=== Done ===
 
:* Workflow for individual analysis (Slicer3 external module using BatchMake)
 
:* Workflow for individual analysis (Slicer3 external module using BatchMake)
−
:* Workflow for group analysis (KWWidgets application 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"
−
:* Regional analysis to compare our method with results from FreeSurfer
+
:* Pediatric and adult brain atlases available to the community via MIDAS
−
:* Tutorials
+
:* ARCTIC available to the community via NITRC: executables (UNC external modules for Slicer3) and Tutorial dataset
 +
:** New version including quality control through MRML scene, and WM, GM models generation
 +
:** Mac and windows executables also available on to the community through NITRC
 +
:** Input images orientation automatically managed by ARCTIC
 +
:* ARCTIC source code (SVN, CVS) available to the community
 +
:* ARCTIC executable can be downloaded directly within Slicer3 using the extension wizard
 +
:* Comparison to FreeSurfer (40 cases dataset): pearson correlation analysis
 +
 
 +
== References ==
  
−
=== Future work ===
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*Hazlett HC, Poe MD, Gerig G, Styner M, Chappell C, Smith RG, Vachet, C., & Piven, J., Early brain overgrowth in autism results from an increase in cortical surface area before age 2, Arch Gen Psychiatry. 2011;68(5):1-10
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:* UNC Slicer3 external modules available on NITRIC
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*H.C. Hazlett, C. Vachet, C. Mathieu, M. Styner, J. Piven, Use of the Slicer3 Toolkit to Produce Regional Cortical Thickness Measurement of Pediatric MRI Data, presented at the 8th Annual International Meeting for Autism Research (IMFAR) Chicago, IL 2009.
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:* Pediatric atlases (T1-weighted image, parcellation map, probability maps) available to the community (NITRIC or PubDB?)
+
*C. Mathieu, C. Vachet, H.C. Hazlett, G. Geric, J. Piven, and M. Styner, ARCTIC – Automatic Regional Cortical ThICkness Tool, UNC Radiology Research Day 2009 abstract

Latest revision as of 17:46, 10 July 2017

Home < DBP2:UNC:Regional Cortical Thickness Pipeline
Back to  UNC Cortical Thickness Roadmap
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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 measurement
    • 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.

Download

ARCTIC download

Source code, executables and tutorial are available on NITRC

Complementary downloads

Brain atlases

Four brain atlases are available on MIDAS:

Pediatric Brain MRI data

Data of 2 autistic children and 2 normal controls (male, female) scanned at 2 years with follow up at 4 years from a 1.5T Siemens scanner. Files include structural data, tissue segmentation label map and subcortical structures segmentation.

Tutorials

  • ARCTIC tutorial : end-to-end Slicer3 module to perform automatic regional cortical thickness analysis‏ [ppt]‏ [pdf]
    • 1st Prize: NAMIC tutorial contest AHM 2009
    • 2nd Prize: NAMIC tutorial contest summer project week 2009
  • 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 performed a regional statistical analysis based on Pearson's correlation on an adult dataset (FreeSurfer's publicly available dataset) including 40 cases. We also performed tests on a longitudinal autism study of 86 subject aged 2-4 years.

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 and adult brain atlases available to the community via MIDAS
  • ARCTIC available to the community via NITRC: executables (UNC external modules for Slicer3) and Tutorial dataset
    • New version including quality control through MRML scene, and WM, GM models generation
    • Mac and windows executables also available on to the community through NITRC
    • Input images orientation automatically managed by ARCTIC
  • ARCTIC source code (SVN, CVS) available to the community
  • ARCTIC executable can be downloaded directly within Slicer3 using the extension wizard
  • Comparison to FreeSurfer (40 cases dataset): pearson correlation analysis

References

  • Hazlett HC, Poe MD, Gerig G, Styner M, Chappell C, Smith RG, Vachet, C., & Piven, J., Early brain overgrowth in autism results from an increase in cortical surface area before age 2, Arch Gen Psychiatry. 2011;68(5):1-10
  • H.C. Hazlett, C. Vachet, C. Mathieu, M. Styner, J. Piven, Use of the Slicer3 Toolkit to Produce Regional Cortical Thickness Measurement of Pediatric MRI Data, presented at the 8th Annual International Meeting for Autism Research (IMFAR) Chicago, IL 2009.
  • C. Mathieu, C. Vachet, H.C. Hazlett, G. Geric, J. Piven, and M. Styner, ARCTIC – Automatic Regional Cortical ThICkness Tool, UNC Radiology Research Day 2009 abstract
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