For more information about the specification of BIDS Apps see here.
🔪 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:
- Pydeface
- MRI deface
- De-identification toolbox
- Quickshear
Using BIDSonym ensures that you can make collected neuroimaging data available for others without violating subjects' privacy or anonymity.
Provide a link to the documention of your pipeline.
Provide instructions for users on how to get help and report errors.
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.)
This App has the following command line arguments:
usage: run.py [-h]
[--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
bids_dir output_dir {participant,group}
Example BIDS App entry point script.
positional arguments:
bids_dir The directory with the input dataset formatted
according to the BIDS standard.
output_dir The directory where the output files should be stored.
If you are running a group level analysis, this folder
should be prepopulated with the results of
the participant level analysis.
{participant,group} Level of the analysis that will be performed. Multiple
participant level analyses can be run independently
(in parallel).
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 will be analyzed. Multiple participants
can be specified with a space separated list.
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
Describe whether your app has any special requirements. For example:
- Multiple map reduce steps (participant, group, participant2, group2 etc.)
- Unusual memory requirements
- etc.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent