Recent advances in magnetic resonance (MR) scanner quality and the rapidly improving nature of facial recognition software have necessitated the introduction of MR defacing algorithms to protect patient privacy. As a result, there are a number of MR defacing algorithms available to the neuroimaging community, with several appearing in just the last five years. These various approaches have qualities that have been explored with respect to skull stripping masks or identifiability of the patient in previous works. However, to our knowledge there has been no evaluation of the subsequent impact of these defacing algorithms on a neuroimaging pipeline. In this work, we use six MR defacing algorithms on 179 subjects from the OASIS-3 cohort and 21 subjects from the Kirby 21 dataset, then apply a neuroimaging pipeline to the resultant defaced images. We compare the consistency of the output from the pipeline using the defaced images with the output of the same pipeline without defacing the MR data.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.