BIDSonym

BIDSonym: a BIDS App for the pseudo-anonymization of neuroimaging datasets - Published in JOSS (2021)

https://github.com/peerherholz/bidsonym

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a BIDS app for pseudo-anonymization of neuroimaging data

README.rst

===============================
BIDSonym
===============================

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Description
===========
A `BIDS `_ `App `_ for the de-identification of neuroimaging data. ``BIDSonym`` gathers all T1w images from a BIDS dataset and applies one of several popular de-identification algorithms. It currently supports:

`MRI deface `_, `Pydeface `_, `Quickshear `_ and `mridefacer `_.

.. image:: https://raw.githubusercontent.com/PeerHerholz/BIDSonym/master/img/bidsonym_example.png
   :alt: alternate text

Additionally, the user can choose to evaluate the sidecar JSON files regarding potentially sensitive information,
like for example participant names and define a list of fields which information should be deleted.

**Using BIDSonym can help you make collected neuroimaging data available for others without violating subjects' privacy or anonymity (depending on the regulations of the country you're in).**

.. intro-marker

Usage
=====

.. usage-marker

This App has the following command line arguments:

.. code-block::

	usage:	run.py [-h]

	[--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]

	[--deid {pydeface,mri_deface,quickshear}]

	[--del_nodeface {del,no_del}]

	[--deface_t2w]

	[--check_meta]

	[--del_meta META_DATA_FIELD [META_DATA_FIELD ...]]

	[--brainextraction {bet,nobrainer}]

	[--bet_frac BET_FRAC]

	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:
	  --participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]
				The label(s) of the participant(s) that should be
				analyzed. The label corresponds to
				sub- 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.
	  --deface_t2w \
		    Deface T2w images by using defaced T1w image as deface-mask.
	  --check_meta META_DATA_FIELD [META_DATA_FIELD ...]  
		    Indicate which information from the image and
		    :code:`.json` meta-data files should be check for potentially problematic information. 
		    Indicate strings that should be searched for.
		    The results will be saved to :code:`sourcedata/`.
	  --del_meta META_DATA_FIELD [META_DATA_FIELD ...]
				Indicate (via strings) if and which information from the :code:`.json` meta-data
				files should be deleted. If so, the original :code:`.json` files
				will be copied to :code:`sourcedata/`.
	  --brainextraction {BET, no_brainer}
				What algorithm should be used for pre-defacing brain extraction
				(outputs will be used in quality control).
	  --bet_frac [BET_FRAC]
				In case BET is used for pre-defacing brain extraction, provide a Frac value.
	  --skip_bids_validation \
		    Assume the input dataset is BIDS compliant and skip the validation (default: False).
	  -v \
	    BIDS-App version.


Run it in participant level mode (for one participant):

.. code-block::

	docker run -i --rm \
		    -v /Users/peer/ds005:/bids_dataset \
	            peerherholz/bidsonym \
		    /bids_dataset \
		    participant --deid pydeface --del_meta 'InstitutionAddress' \
		    --participant_label 01
		    --brainextraction bet --bet_frac 0.5


Run it in group level mode (for all participants):

.. code-block::

	docker run -i --rm \
		   -v /Users/peer/ds005:/bids_dataset \
		   peerherholz/bidsonym \
		   /bids_dataset  group --deid pydeface --del_meta 'InstitutionAddress' \
		   --brainextraction bet --bet_frac 0.5

.. usage-marker-end


Installation
============
Following the `BIDS apps standard `_ it is recommend to install and use BIDSonym in its Docker or Singularity form. \
To get the BIDSonym Docker image, you need to `install docker `_ and within the terminal of your choice type:

:code:`docker pull peerherholz/bidsonym`


Documentation
=============
BIDSonym's documentation can be found `here `_.


How to report errors
====================
Running into any bugs :beetle:? 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
================
Please acknowledge this work by mentioning explicitly the name of this software
(*BIDSonym*) and the version, along with a link to the `GitHub repository
`_ or the Zenodo reference.
For more details, please see `citation `_.

Support
=======
This work is supported in part by funding provided by `Brain Canada `_, in partnership with `Health Canada `_, for the `Canadian Open Neuroscience Platform initiative `_.

.. image:: https://conp.ca/wp-content/uploads/elementor/thumbs/logo-2-o5e91uhlc138896v1b03o2dg8nwvxyv3pssdrkjv5a.png
    :alt: logo_conp
    :target: https://conp.ca/

Furthermore, the project is supported by `Repronim `_ under NIH-NIBIB P41 EB019936. 

Owner

JOSS Publication

BIDSonym: a BIDS App for the pseudo-anonymization of neuroimaging datasets

Published

August 28, 2021

DOI

10.21105/joss.03169

Volume 6, Issue 64, Page 3169

Authors

Peer Herholz
NeuroDataScience-Origami lab, McConnell Brain Imaging Centre, The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine, McGill University, Montreal, Quebec, Canada

Rita Marie Ludwig
University of Oregon, Eugene, United States

Jean-Baptiste Poline
NeuroDataScience-Origami lab, McConnell Brain Imaging Centre, The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine, McGill University, Montreal, Quebec, Canada

Editor

Olivia Guest

Tags

data pseudo-anonymization - neuroimaging datasets - de-identification - data sharing

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