CIVET

What is CIVET?

 
CIVET is an image processing pipeline for fully automated volumetric, corticometric, and morphometric analysis of human brain imaging data (MRI). CIVET performs transformation into stereotaxic space, gray and white matter tissue classification, reconstruction of left and right hemisphere cortical surfaces, surface registration in order to perform group comparisons and cortical thickness analysis. Regional maps are produced based on the lobar parcellation of surfaces. A number of other measurements are also performed, such as mean curvature, gyrification index and total cortical area.
 

 

CIVET as an Image Processing Pipeline

 
CIVET is also an image processing pipeline for rapid, efficient and distributed processing of multiple data sets. This is made possible by the automated image processing “pipeline” concept and framework, using a simple definition of the pipeline structure, combined with a compute cluster and a job scheduler. Since it was developed, hundreds of scientific articles have been published with analyses performed using CIVET.
 

Technical Features of CIVET

 
CIVET is implemented with high-level scripts, primarily using the common scripting language Perl, that runs computationally efficient image processing tools mainly implemented in C/C++. CIVET extends the previous in-house pipelines by the addition of corticometry analysis tools.
 

Image Processing Tools

 
CIVET analysis includes our in-house morphometric analysis tools for:

 

CIVET Outputs

 
CIVET produces outputs for the following statistical analysis:

  • Voxel-Based Morphometry (VBM)
  • Cortical thickness analysis
  • Surface features

CIVET also produces:

  • A set of quality control images
  • Quality control data tables
  • Log files showing completion of processing steps

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CIVET2_s0004_o2_atlas

AAL surface paracellation

 

CIVET2_s0004_o2_clasp

CLASP surfaces

 

More Information and Documentation

 
Documentation about CIVET at McGill University website

Using CIVET on CBRAIN platform

 

Key Publications

 

  • Boucher M et al (2009) Depth potential function for folding pattern representation, registration and analysis. Med Image Anal [full ref]
  • Chung MK et al (2005) Unified statistical approach to cortical thickness analysis. Inf Process Med Imaging [full ref]
  • Chung MK et al (2005) Cortical thickness analysis in autism with heat kernel smoothing. Neuroimage [full ref]
  • Im K et al (2008) Brain size and cortical structure in the adult human brain. Cereb Cortex [ful ref]
  • Kabani N et al (2001) Measurement of cortical thickness using an automated 3-D algorithm: a validation study. Neuroimage [full ref]
  • Kim JS et al (2005) Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. Neuroimage [full ref]
  • Lee JK et al (2006) A novel quantitative cross-validation of different cortical surface reconstruction algorithms using MRI phantom. Neuroimage [full ref]
  • Lerch JP, Evans AC (2005) Cortical thickness analysis examined through power analysis and a population simulation. Neuroimage [full ref]
  • Lerch JP et al (2005) Focal decline of cortical thickness in Alzheimer’s disease identified by computational neuroanatomy. Cereb Cortex [full ref]
  • Lerch JP et al (2006) Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. Neuroimage [full ref]
  • Lyttelton O et al (2007) An unbiased iterative group registration template for cortical surface analysis. Neuroimage [full ref]
  • MacDonald D et al (2000) Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. Neuroimage [full ref]
  • Robbins S et al (2004) Tuning and comparing spatial normalization methods. Med Image Anal [full ref]
 
 
 
 
Quality control

Quality control