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ARCTIC: Automatic Regional Cortical ThICkness
Module Type & Category
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
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.
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.
Quick Tour of Features and Use
- Inputs: Two modes exist to select input images needed to perform the cortical thickness analysis
- Input mode 1 - Raw Images: set input images representing the different modalities of the same subject
- T1-weighted image: set input T1 image, if available
- 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 segmentation atlas type (should match subject input image)
- Input mode 2 - Segmented Images: set tissue segmentation label image with its related raw image
- 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 a lobar analysis, i.e to perform the atlas warping)
- 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 the output images
- Parcellation: set parameters to perform a lobar analysis
- If a parcellation is already defined for the subject:
- Case parcellation image: set the parcellation image defined in the subject space coordinate
- If a parcellation needs to be defined (i.e atlas registration will be performed):
- Atlas parcellation image: set the atlas parcellation image
- Atlas image: set the grayscale atlas image
ARCTIC Advanced Parameters User Interface
- Advanced tissue segmentation parameters
- Filter options: specifies smoothing parameters prior to the segmentation
- Priors: set prior weights for priors defined in the atlas directory. Prior weights can be adjusted globally (e.g., if one class overwhelms the other it can be set a lower weight) through comma separated numbers.
- Maximum bias degree: set bias correction polynomial degree to adjust the severity of the bias field in the image. Bias field correction can be disabled by setting it to zero.
- Atlas warping parameters: enable/disable B-spline warping and set grid size per direction (number of control points).
- Advanced skull-stripping parameters
- Mask dilation (default: disabled): check button to slightly dilate the mask. Useful if the tissue segmentation doesn't perfectly mask the brain.
- Advanced cortical thickness parameters:
- InterpOff (default: disabled): check button to gather interpolated cortical thickness measurements in two additionnal csv files. The method computes sparse measurement by default.
- Threshold (default: 1.8): set distance threshold (in mm) used to match the cortical thickness map with the parcellation
- Advanced atlas registration parameters:
- Initialization: choose between different initialization methods (see registration pipeline discussion)
Notes from the Developer(s)
Algorithms used, library classes depended upon, use cases, etc.
- Slicer3 modules:
- Publicly available atlases on MIDAS:
On the Dashboard, these tests verify that the module is working on various platforms:
Links to known bugs in the Slicer3 bug tracker
Follow this link to the Slicer3 bug tracker. Please select the usability issue category when browsing or contributing.
Source code & documentation
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
- 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