a BIDS app for de-identification of neuroimaging data
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README.md

BIDSonym

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Description

🔪 🔪 A BIDS app for de-identification of neuroimaging data. Takes BIDS-format T1 and T2-weighted images and applies one of several popular de-identification algorithms. BIDSonym currently supports:

bidsonym example

Using BIDSonym ensures that you can make collected neuroimaging data available for others without violating subjects' privacy or anonymity.

Documentation

Provide a link to the documention of your pipeline.

How to report errors

Running into any bugs 🐞? Check out the open issues to see if we're already working on it. If not, open up a new issue and we will check it out when we can!

How to contribute

Thank you for considering contributing to our project! Before getting involved, please review our Code of Conduct. Next, you can review open issues that we are looking for help with. If you submit a new pull request please be as detailed as possible in your comments. Please also have a look at our contribution guidelines.

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]
              [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
              [--deid {pydeface,mri_deface,quickshear}]
              [--del_nodeface {del,no_del}]
              bids_dir {participant,group}

a BIDS app for de-identification of neuroimaging data

positional arguments:
  bids_dir              The directory with the input dataset formatted
                        according to the BIDS standard.
  output_dir            The directory where the not de-identified raw files should be stored,
		        in case you decide to keep them.
  {participant,group}   Level of the analysis that will be performed. Multiple
                        participant level analyses can be run independently
                        (in parallel) using the same output_dir.

optional arguments:
  -h, --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.
  --deid {pydeface,mri_deface,quickshear}
                        Approach to use for de-identifictation.
  --del_nodeface {del,no_del}
                        Overwrite and delete original data or copy original
                        data to different folder.

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

docker run -i --rm \
	-v /Users/filo/data/ds005:/bids_dataset \
	bids/bidsonym \
	/bids_dataset participant --deid pydeface --del_nodeface no_del --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 \
	bids/bidsonym \
	/bids_dataset  group --deid pydeface --del_nodeface no_del