This repository contains all scripts, images and the Docker recipe to build the BIDS compliant Cerebellar Volume Extraction Tool (CVET) Docker image.
CVET takes in T1-weighted imaging data and returns a spreadsheet of cerebellar volumes for each lobule according to the SUIT atlas (Diedrichsen et al., 2009), a gray matter map of the cerebellum in MNI space for subsequent voxel-wise analysis, and a processing report for quality control purposes.
CVET pipeline applies FreeSurfer 6.0.0 (Fischl, 2004) to create an initial cerebellum mask and to obtain the estimated total intracranial volume (eTIV). Subsequently, it uses advanced normalization tools (ANTs) N4 bias field correction (Tustison et al., 2010) to adjust the T1 for MR field inhomogeneity. In a next step gray matter volume maps are obtained using statistical parametric mapping (SPM12; Ashburner, 2012). ANTs with symmetric normalization (SyN) as the transformation algorithm (Avants et al., 2011) is then used to bring a spatially unbiased infratentorial template (SUIT) into the native image space of the subject. This SUIT template corresponds to a SUIT atlas of 28 cerebellar hemispheric and vermis regions that are then used to extract the gray matter volume from the gray matter map that was created in a previous step.
When longitudinal data is detected, a longitudinal pipeline is being run that applies longitudinal FreeSurfer after which it uses the output of this pipeline to generate a subject specific cerebellar template using ANTs’ antsMultivariateTemplateConstruction2.sh script. This unbiased subject template is then used as an intermediate space to move the cerebellar gray matter maps of each time point for a given subject into SUIT template space, and to bring the SUIT atlas to the gray matter maps for each time point.
After the processing, an HTML overview is generated for quality control purposes. This overview displays coronal, sagittal, and axial montages of the outline of the cerebellar mask (see below), gray matter segmentation (see below), lobule segmentation (see below), as well as normalization overviews, subject template overviews and a plot of volume over time for all lobules (the latter two for longitudinal processing only).
