Pipeline for creating a bullseye parcellation of the cerebral whiter matter using part of the FreeSurfer output and commands. It provides an (anatomy-independent) spatial localization based on an radial component (ie, lobes) and a depth component. It can be used to obtain region-specific quantification of white matter parameters (eg, a similar approach has been used to quantify regional white matter hyperintensity load in this and this papers).
This document explains how to use the package. The internals of the process are explained in this blog post.
FreeSurfer 6.0.0
installationPython 2.7
(with packages nibabel
, nipype
, numpy
and scipy
)After cloning the repository, install the pipeline with python setup.py install
When the pipeline is installed, it can then be executed from the command line as run_bullseye_pipeline -h / [ARGS]
run_bullseye_pipeline
accepts the following arguments:
-s
, --scansdir
: scans directory with the data for each subject (mandatory)-w
, --workdir
: work directory where data will be processed' (mandatory)-o
, --output_dir
: output directory where results will be stored (mandatory)--subjects
: one or more subject IDs (space separated)-b
, --debug
: debug mode (saves the pipeline graph in the work directory)-p
, --processes
: overall number of parallel processes-n
, --name
: pipeline workflow name (default='bullseye_pipeline')The pipeline can be run by passing an input scans directory following the FreeSurfer structure (typically pointed by SUBJECTS_DIR
).
However, not all the FreeSurfer output is required.
The following is the mandatory portion of FreeSurfer data that is required by the pipeline:
scansdir
subject-id1
mri
aseg.mgz
ribbon.mgz
label
lh.aparc.annot
rh.aparc.annot
surf
lh.white
rh.white
lh.pial
rh.pial
subject-id2
After execution of the pipeline a directory output_dir
is created with the following structure:
output_dir
subject-id1
bullseye_wmparc.nii.gz
lobes_wmparc.nii.gz
shells_wmparc.nii.gz
subject-id2
containing, respectively, the final bullseye parcellation, and the lobar and depth parcellations used to create it.