BIDS-Apps/MAGeTbrain

Name: MAGeTbrain

Owner: BIDS Apps

Description: null

Created: 2016-09-19 21:57:37.0

Updated: 2018-04-23 17:38:43.0

Pushed: 2018-04-23 18:27:33.0

Homepage: null

Size: 27

Language: Python

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README

MAGeTbrain segmentation pipeline

Description

This pipeline takes in native-space T1 or T2 (or multiple co-registered modalities) brain images and volumetrically segments them using the MAGeTbrain algorithm.

Documentation

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How to report errors

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Acknowledgements

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Usage

This App has the following command line arguments:

e: run.py [-h]
          [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
          [--segmentation_type {amygdala,cerebellum,hippocampus-whitematter,colin27-subcortical,all}]
          [-v] [--n_cpus N_CPUS] [--fast] [--label-masking] [--no-cleanup]
          bids_dir output_dir {participant1,participant2,group}

Tbrain BIDS App entrypoint script.

tional arguments:
ds_dir              The directory with the input dataset formatted
                    according to the BIDS standard.
tput_dir            The directory where the output files should be stored.
                    When you are running group level analysis this folder
                    must be prepopulated with the results of
                    theparticipant level analysis.
articipant1,participant2,group}
                    Level of the analysis that will be performed. Multiple
                    participant level analyses can be run independently
                    (in parallel) using the same output_dir. In MAGeTbrain
                    parlance, participant1 = template stage, partipant2 =
                    subject stage group = resample + vote + qc stage. The
                    proper order is participant1, participant2, group

onal arguments:
, --help            show this help message and exit
participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]
                    The label(s) of the participant(s) that should be
                    analyzed. The label corresponds to
                    sub-<participant_label> from the BIDS spec (so it does
                    not include "sub-"). If this parameter is not provided
                    all subjects should be analyzed. Multiple participants
                    can be specified with a space separated list.
segmentation_type {amygdala,cerebellum,hippocampus-whitematter,colin27-subcortical,all}
                    The segmentation label type to be used.
                    colin27-subcortical, since it is on a different atlas,
                    is not included in the all setting and must be run
                    seperately
, --version         show program's version number and exit
n_cpus N_CPUS       Number of CPUs/cores available to use.
fast                Use faster (less accurate) registration calls
label-masking       Use the input labels as registration masks to reduce
                    computation and (possibily) improve registration
no-cleanup          Do no cleanup intermediate files after group phase

To run it in participant level mode (for one participant):

docker run -i --rm \
    -v /Users/filo/data/ds005:/bids_dataset:ro \
    -v /Users/filo/outputs:/outputs \
    bids/example \
    /bids_dataset /outputs participant --participant_label 01

After doing this for all subjects (potentially in parallel), the group level analysis can be run:

docker run -i --rm \
    -v /Users/filo/data/ds005:/bids_dataset:ro \
    -v /Users/filo/outputs:/outputs \
    bids/example \
    /bids_dataset /outputs group

Special considerations

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• Multiple map reduce steps (participant, group, participant2, group2 etc.)
• Unusual memory requirements
• etc.