MAGeTbrain segmentation pipeline

Description

This pipeline takes in native-space T1 brain images and volumetrically segments them using the MAGeTbrain algorithm using a variety of input atlases.

Documentation

https://github.com/cobralab/antsRegistration-MAGet.

How to report errors

Please open an issue at https://github.com/BIDS-Apps/MAGeTbrain/issues

Acknowledgements

Describe how would you would like users to acknowledge use of your App in their papers (citation, a paragraph that can be copy pasted, etc.)

Usage

This App has the following command line arguments:

``` usage: run.py [-h] [--participantlabel PARTICIPANTLABEL [PARTICIPANTLABEL ...]] [--segmentation_type {amygdala,cerebellum,hippocampus-whitematter,colin27-subcortical,all}] [-v] [--ncpus NCPUS] [--fast] [--label-masking] [--no-cleanup] bidsdir output_dir {participant1,participant2}

MAGeTbrain BIDS App entrypoint script.

positional arguments: bidsdir The directory with the input dataset formatted according to the BIDS standard. outputdir The directory where the output files should be stored. When you are running partipant2 level analysis this folder must be prepopulated with the results of the participant1 level analysis. {participant1,participant2} Level of the analysis that will be performed. Multiple participant{1,2} level analyses can be run independently (in parallel) using the same output_dir. In MAGeTbrain parlance, participant1 = template stage, partipant2 = subject + resample + vote + qc stage. The proper order is participant1, participant2

optional arguments: -h, --help show this help message and exit --participantlabel PARTICIPANTLABEL [PARTICIPANTLABEL ...] The label(s) of the participant(s) that should be analyzed. The label corresponds to sub-<participantlabel> 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. --segmentationtype {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 separately -v, --version show program's version number and exit --ncpus 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 (possibly) improve registration --no-cleanup Do no cleanup intermediate files after group phase ```

To run construct the template library, run the participant1 stage: sh docker run -i --rm \ -v /Users/filo/data/ds005:/bids_dataset:ro \ -v /Users/filo/outputs:/outputs \ bids/example \ /bids_dataset /outputs participant1 --participant_label 01

After doing this for approximately 21 representative subjects (potentially in parallel), the subject level labeling can be done: can be run: sh docker run -i --rm \ -v /Users/filo/data/ds005:/bids_dataset:ro \ -v /Users/filo/outputs:/outputs \ bids/example /outputs participants2 --participant_label 01 This can also happen in parallel on a per-subject basis

Special considerations

• segmentation_types output directories must be kept separate for each type
• participant1 stages can be run in parallel per subject, approximately 21 subjects should be selected which are a representative subset of the population under study
• participant2 stages can also be run in parallel, but must be started after participant1 stages are complete