Analysis tools for sub-millimetre MRI of the cerebral cortex
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This is a collection of software designed to process 3D images of the cerebral cortex at a sub-millimetre scale, for example high-resolution MRI. In particular, it implements Bok’s equivolumetric model for extracting cortical layers while compensating for cortical curvature.

If you use this work in an academic publication, please cite the relevant references (see doc/references.bib).

Basic usage

This package can be used on the command line, here is a short introduction. See below for installation instructions.

  1. Set up the necessary environment using

    . </path/to/installation>/bin/
  2. Prepare your input data: the input that is common to to all processes is classif: a voxel-wise tissue classification image in signed 16-bit pixel type, with 0 for exterior voxels (CSF), 100 for cortical gray matter, and 200 for subcortical white matter.

  3. Run the process that you are interested in. The common interface to all processes is the Capsul command-line, which you can call with python -m capsul (or python -m for Capsul >= 2.1.3). Use python -m capsul --process-help <process_name> to get help for a specific process. Use python -m capsul <process_name> [parameter=value ...] to run a process.

    The most important processes are described below:

    • Equivolumetric depth according to Bok’s model can be computed with highres_cortex.capsul.isovolume. The only mandatory input is classif, the output is equivolumetric_depth. You can fine-tune the process with optional parameters, most importantly advection_step_size can be adapted to the spatial resolution and required accuracy. For example:

      python -m capsul highres_cortex.capsul.isovolume classif=classif.nii.gz advection_step_size=0.03 equivolumetric_depth=equivolumetric_depth.nii.gz
    • Cortical thickness, according to the Laplace model, can be calculated with two different methods:

      • The upwinding method is very fast, and already has sub-pixel accurracy: highres_cortex.capsul.thickness_upw. The only mandatory input is classif, the output is thickness_image.
      • The advection method is slower, but advection_step_size can be tuned for greater accuracy: highres_cortex.capsul.thickness_adv.
    • For parcellating the cortex into volumetric traverses, highres_cortex.capsul.traverses can be used. The only mandatory input is classif, the output is cortical_traverses. The goal_diameter parameter controls the target diameter of merged regions (in millimetres). The advection_step_size parameter is also relevant for this process.

If you have used highres-cortex before the Capsul interface was introduced (beginning of 2018), you may be using the old shell scripts. See examples/scripts/ for equivalent scripts that make use of the Capsul processes.


Binary packages

We are planning on packaging highres-cortex, and making it available as part of the graphical installer of BrainVISA. For now you have to compile it as described below.

Automated compilation

The script can be used to download, configure, and build highres-cortex:

chmod +x

The script is interactive, and will require some input from you.

  • If you are on Ubuntu 14.04 or 16.04 LTS, this script will ensure that all the required dependencies are present on your machine, and propose their installation otherwise. For any other distribution, or any other version of Ubuntu, you have to install the dependencies manually.
  • You will also be asked for a base directory to contain the downloaded sources, and the build process. You will need about 1.5 GB of free space in this directory.

The step-by-step approach in the next section performs essentially the same steps as the bootstrapping script, use that if you want to customize the build process.

Step-by-step compilation

You can compile this package as part of the BrainVISA source tree, which is based on CMake and uses a custom-made driver called bv_maker.

  1. Install the dependencies. Under Ubuntu, the required packages are: subversion git cmake make gcc g++ gfortran pkg-config libblitz0-dev libsigc++-2.0-dev libxml2-dev libqt4-dev libboost-dev zlib1g-dev libtiff-dev python2.7-dev python-sip-dev python-numpy python-six libqt4-sql-sqlite.

  2. Bootstrap the bv_maker tool:

    svn export --username brainvisa --password Soma2009 /tmp/brainvisa-cmake
    cd /tmp/brainvisa-cmake
    make install

    Detailed instructions can be found in this Introduction to bv_maker (login brainvisa, password Soma2009).

  3. Create the configuration file for bv_maker at $HOME/.brainvisa/bv_maker.cfg. Here is a minimal version of this file:

    [ source $HOME/brainvisa/source ]
      brainvisa brainvisa-cmake bug_fix
      brainvisa soma-base bug_fix
      brainvisa soma-io bug_fix
      brainvisa aims-free bug_fix
      brainvisa soma-workflow $CASA_BRANCH
      brainvisa capsul $CASA_BRANCH
      git master highres-cortex
    [ build $HOME/brainvisa/build ]
      build_type = Release
      brainvisa-cmake bug_fix $HOME/brainvisa/source
      brainvisa-share bug_fix $HOME/brainvisa/source
      soma-base bug_fix $HOME/brainvisa/source
      soma-io bug_fix $HOME/brainvisa/source
      aims-free bug_fix $HOME/brainvisa/source
      + $HOME/brainvisa/source/highres-cortex
    Keep the following in mind if you want to customize this configuration file:
    • you need this line in the source section:

      git master highres-cortex
    • you need this line in the build section:

      + </path/to/brainvisa/source>/highres-cortex
    • you need to enable the aims-free component and its dependencies brainvisa-cmake, soma-base, and soma-io; alternatively, just enable the anatomist group, which is a superset of these.

  4. Run /tmp/brainvisa-cmake/bin/bv_maker, which will check out a local copy of the sources, configure them with cmake, and build thim with make.

  5. You can then run the software directly from $HOME/brainvisa/build, as indicated in the Basic usage section.


  • AIMS version 4.5 or later, an image processing library distributed as part of BrainVISA.
  • Boost version 1.49 or later.
  • Python version 2.6 or later.
  • CMake version 2.6 or later, with its extension brainvisa-cmake (distributed with BrainVISA).
  • Recommended: Capsul version 2 or later, used to combine the low-level building blocks into useful processing pipelines.
  • Optional: the VipHomotopic command-line tool from the Morphologist image segmentation pipeline, distributed as a binary only tool with the BrainVISA installer.


The source code of this work is placed under the CeCILL licence (see LICENCE.CeCILL.txt). This library contains code that is under the GNU LGPL licence (see src/library/cortex_column_region_quality.tcc), as a result, compiled code must be redistributed under the GNU General Public Licence (see LICENCE.GPLv3.txt).

External code used in this repository

  • Code for numerical diagonalization of 3×3 matrices (src/library/cortex_column_region_quality.tcc) is Copyright 2006 Joachim Kopp, under the GNU LGPL v2.1 or later. Reference: Kopp, Joachim. ‘Efficient Numerical Diagonalization of Hermitian 3x3 Matrices’. International Journal of Modern Physics C 19, no. 03 (March 2008): 523–48. arXiv:physics/0610206.