CIVET is a human brain image-processing pipeline for fully-automated corticometric, morphometric and volumetric analyses of magnetic resonance (MR) images. The pipeline is composed of a set of Perl scripts and high-level C/C++ programs.
CIVET starts by a 9 or 12-parameter linear/affine registration of the MR images (T1-weighted, T2-weighted and Proton Density weighted) from native to stereotaxic space, using the average MNI ICBM152 model as the target of registration. Once the images have been transformed to stereotaxic space, the images are corrected for radio-frequency non-uniformities and a brain mask is computed. A non-linear transformation is then computed from the subject in stereotaxic space to the model.
The next step in CIVET is to perform the tissue classification into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), either based on the T1-weighted image only, or on multispectral data (T1, T2, PD). The classification is comprised of two steps: a discrete classification based on tag points, followed by the evaluation of the partial volumes for the tissue classes.
The brain is split into the left and right hemispheres for the purpose of surface extraction. The boundary between cortical GM and subcortical WM (referred to as the white matter surface) is extracted using a deformable surface model, starting from an ellipsoid that contracts to take the shape of the white matter mask. The pial surface, or the boundary between the cortical GM and the extra-cortical CSF (referred to as the GM surface) expands outwards from the WM surface to the CSF. Each surface is composed of 81,920 triangles (polygons) and 40,962 vertices.
The surfaces are registered to the MNI ICBM152 surface template. Surface registration as such allows for group comparisons by interpolating vertex-based cortical measurements at reference vertices on the template. The cortical thickness is computed by evaluating the distance, in mm, between the original WM and GM surfaces transformed back to the native space of the original MR images, then interpolated onto the surface template. Vertex-based areas and volumes are, on the other hand, computed directly on the resampled surfaces and measure local variations of area/volume contraction and expansion relative to the vertex distribution on the surface template.
Regional maps are produced based on a lobar parcellation of the surfaces (major lobes or AAL). Other measurements such as mean curvature, gyrification index, and total cortical area are also computed.
A number of figures and reports are automatically produced to assist in the process of quality control.