Difference between revisions of "Modules:ARCTIC-Documentation-3.6"

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* '''Input 1: Raw Images'''
 
* '''Input 1: Raw Images'''
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** T1-weighted image:
+
** T1-weighted image: set input T1 image
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** T2-weighted image:
+
** T2-weighted image: set input T2 image, if available
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** PD-weighted image:
+
** PD-weighted image: set input PD image, if available
−
** Tissue segmentation atlas directory:
+
** Tissue segmentation atlas directory: set directory containing gray-scale atlas to perform the tissue segmentation
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** Segmentation atlas type (T1 or T2, default: T1): select at
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** Segmentation atlas type (T1 or T2, default: T1): set atlas type
 
* '''Input 2: Segmented Images'''
 
* '''Input 2: Segmented Images'''
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** Tissue segmentation image:
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** Tissue segmentation image: set tissue segmentation label image
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** White matter label (default: 1)
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** White matter label (default: 1): set tissue segmentation white matter label
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** Gray matter label (default: 2)
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** Gray matter label (default: 2): set tissue segmentation gray matter label
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** CSF label:
+
** CSF label (default: 3): set tissue segmentation CSF label
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** Raw image:  
+
** Raw image: set its related raw image (needed to perform the lobar analysis, i.e to perform the atlas orientation)
 
* '''Output:'''
 
* '''Output:'''
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** Cortical thickness on white matter boundary:
+
** Cortical thickness on white matter boundary: set output image which represents cortical thickness measurement on the white matter boundary
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** Cortical thickness on gray matter boundary
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** Cortical thickness on gray matter boundary: set output image which represents cortical thickness measurement on the gray matter boundary
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** Cortical thickness results directory:
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** Cortical thickness results directory: set output directory to save cortical thickness measurements
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** ID number:
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** ID number: set ID number to set prefix of output images
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* '''Parcellation:'''
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* '''Parcellation:''': parameters need to be set to perform a lobar analysis
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** Case parcellation image:
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** Parcellation already defined for the subject
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** Atlas parcellation image:
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*** Case parcellation image: set the parcellation image defined in the case space coordinate
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** Atlas image:
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** Parcellation needs to be defined: atlas is registered to the case
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*** Atlas parcellation image: set the atlas parcellation image
 +
*** Atlas image: set the grayscale atlas image
 
** Atlas parcellation orientation:  
 
** Atlas parcellation orientation:  
 
* '''Advanced tissue segmentation parameters'''
 
* '''Advanced tissue segmentation parameters'''

Revision as of 21:57, 31 March 2010

Home < Modules:ARCTIC-Documentation-3.6

Return to Slicer 3.6 Documentation

Gallery of New Features


Module Name

MyModule

ARCTIC logo
T1 skull-stripped image
Parcellation image
Cortical thickness on WM surface
Cortical thickness information

General Information

Module Type & Category

Type: CLI

Category: Pipeline

Authors, Collaborators & Contact

  • Author1: Cedric Mathieu, UNC-Chapel Hill
  • Contributor1: Clement Vachet, UNC-Chapel Hill
  • Contributor2: Martin Styner, UNC-Chapel Hill
  • Contributor3: Heather Cody Hazlett, UNC-Chapel Hill
  • Contact: Clement Vachet, cvachet[at]email[dot]unc[dot]edu

Module Description

ARCTIC (Automatic Regional Cortical ThICkness) is an end-to-end application developped at UNC-Chapel Hill allowing individual analysis of cortical thickness. This cross-platform tool can be run within Slicer3 as an external module, or directly as a command line.

Usage

Use Cases, Examples

This module is especially appropriate when one wants to perform individual regional cortical thickness analysis. ARCTIC allows efficient QC via precomputed 3D-Slicer scenes.

Tutorials

Quick Tour of Features and Use

A list panels in the interface, their features, what they mean, and how to use them. For instance:

  • Input 1: Raw Images
    • T1-weighted image: set input T1 image
    • T2-weighted image: set input T2 image, if available
    • PD-weighted image: set input PD image, if available
    • Tissue segmentation atlas directory: set directory containing gray-scale atlas to perform the tissue segmentation
    • Segmentation atlas type (T1 or T2, default: T1): set atlas type
  • Input 2: Segmented Images
    • Tissue segmentation image: set tissue segmentation label image
    • White matter label (default: 1): set tissue segmentation white matter label
    • Gray matter label (default: 2): set tissue segmentation gray matter label
    • CSF label (default: 3): set tissue segmentation CSF label
    • Raw image: set its related raw image (needed to perform the lobar analysis, i.e to perform the atlas orientation)
  • Output:
    • Cortical thickness on white matter boundary: set output image which represents cortical thickness measurement on the white matter boundary
    • Cortical thickness on gray matter boundary: set output image which represents cortical thickness measurement on the gray matter boundary
    • Cortical thickness results directory: set output directory to save cortical thickness measurements
    • ID number: set ID number to set prefix of output images
  • Parcellation:: parameters need to be set to perform a lobar analysis
    • Parcellation already defined for the subject
      • Case parcellation image: set the parcellation image defined in the case space coordinate
    • Parcellation needs to be defined: atlas is registered to the case
      • Atlas parcellation image: set the atlas parcellation image
      • Atlas image: set the grayscale atlas image
    • Atlas parcellation orientation:
  • Advanced tissue segmentation parameters
  • Advanced skull-stripping parameters
  • Advanced registration parameters:
User Interface

Development

Notes from the Developer(s)

Algorithms used, library classes depended upon, use cases, etc.

Dependencies

Tests

On the Dashboard, these tests verify that the module is working on various platforms:

Known bugs

Links to known bugs in the Slicer3 bug tracker

Usability issues

Follow this link to the Slicer3 bug tracker. Please select the usability issue category when browsing or contributing.

Source code & documentation

Download release source code via svn:

More Information

Acknowledgment

This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics

References

Publications related to this module go here. Links to pdfs would be useful.

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