Full brain parcellation using Geodesic Information Flow

This pipeline uses Geodesic Information Flow (GIF) in order to propagate an atlas parcellation towards a target subject geometry. It only takes an input T1 image as input and a reference database xml file in a specific format. Simplest usage is the following:

usage: perform_gif_propagation_expanded.py [-h] -i input_file [input_file ...]
                                           -d database -o output [-n n_procs]
                                           [-u username] [-g]

GIF Propagation

optional arguments:
  -h, --help            show this help message and exit
  -i input_file [input_file ...], --input_file input_file [input_file ...]
                        Input image file to propagate labels in
  -d database, --database database
                        gif-based database xml file describing the inputs
  -o output, --output output
                        output directory to which the gif outputs are stored
  -n n_procs, --n_procs n_procs
                        maximum number of CPUs to be used when using the
                        MultiProc plugin
  -u username, --username username
                        Username to use to submit jobs on the cluster
  -g, --graph           Print a graph describing the node connections

External links:

• [conference paper about the algorithm]<http://doi.org/10.1007/978-3-642-33418-4_33>