Anonymization tools

Sanitization of headers/filenames

• see http://www.researchgate.net/post/Best_free_tool_for_DICOM_data_anonymization discussion on sanitization of DICOM headers
• DeID (see paper), which provides an interactive tool for inspection and sanitization of Analyze and NIfTI images
• PyDICOM’s deid <https://pydicom.github.io/deid/>, the “best effort anonymization for medical images using python” assists in filtering out DICOM fields and also masking out actual image data

Elimination of facial (and dental) features

Skull stripping

One of the approaches is perform complete skull stripping, e.g. using

• BET of FSL
• 3dSkullStrip of AFNI
• FreeSurfer

Some dedicated anonymization tools work on this principle, e.g. DeID

Faces/dental stripping

More “gentle” approach is to strip out only the areas of face/mouth leaving skull, which might be important for some types of analysis. Usually achieved through alignment of pre-crafted mask to the subject anatomy and removing of the masked out regions.

• mri_deface from FreeSurfer (paper from 2007 with overview)
• https://github.com/poldrack/openfmri/blob/master/pipeline/facemask/deface.py
• https://github.com/hanke/gumpdata/blob/master/scripts/conversion/convert_dicoms_anatomy#L26
• https://github.com/hanke/mridefacer

Rendering faces unrecognizable

Even more data/information preserving approach is to just obscure facial features in the anatomical images:

• Obscuring Surface Anatomy in Volumetric Imaging Data Used for HCP data
• QuickShear