KEYWORDS: Computed tomography, Detection and tracking algorithms, Facial recognition systems, Magnetic resonance imaging, 3D image processing, Head, Brain, Medical imaging, Databases, Control systems
PurposePrevious studies have demonstrated that three-dimensional (3D) volumetric renderings of magnetic resonance imaging (MRI) brain data can be used to identify patients using facial recognition. We have shown that facial features can be identified on simulation-computed tomography (CT) images for radiation oncology and mapped to face images from a database. We aim to determine whether CT images can be anonymized using anonymization software that was designed for T1-weighted MRI data.ApproachOur study examines (1) the ability of off-the-shelf anonymization algorithms to anonymize CT data and (2) the ability of facial recognition algorithms to identify whether faces could be detected from a database of facial images. Our study generated 3D renderings from 57 head CT scans from The Cancer Imaging Archive database. Data were anonymized using AFNI (deface, reface, and 3Dskullstrip) and FSL’s BET. Anonymized data were compared to the original renderings and passed through facial recognition algorithms (VGG-Face, FaceNet, DLib, and SFace) using a facial database (labeled faces in the wild) to determine what matches could be found.ResultsOur study found that all modules were able to process CT data and that AFNI’s 3Dskullstrip and FSL’s BET data consistently showed lower reidentification rates compared to the original.ConclusionsThe results from this study highlight the potential usage of anonymization algorithms as a clinical standard for deidentifying brain CT data. Our study demonstrates the importance of continued vigilance for patient privacy in publicly shared datasets and the importance of continued evaluation of anonymization methods for CT data.
KEYWORDS: Computed tomography, Detection and tracking algorithms, Magnetic resonance imaging, Facial recognition systems, 3D image processing, Brain, Neuroimaging, Medical imaging
Previous studies have demonstrated that 3-dimensional (3D) volumetric renderings of magnetic resonance imaging (MRI) brain scan imaging data can be used to identify patients using facial recognition algorithms. We have shown that facial features can be identified on SIM-CT (simulation computed tomography) images for radiation oncology and mapped to face images from a database. We now seek to determine whether CT images can be anonymized using anonymization software that was designed for T1W MRI data. Our study examines (1) the ability of off-the-shelf anonymization algorithms to anonymize CT data, and (2) the ability of facial recognition algorithms to then identify whether faces could be detected from a database of facial images. This study generated 3D renderings from open-source CT scans of two patients from The Cancer Imaging Archive (TCIA) database. Data were then anonymized using AFNI (deface, reface, 3Dskullstrip), and FSL (deface and BET). Anonymized data were compared to the original renderings and also passed through facial recognition algorithms (Face_compare, VGG-Face, Facenet, DLib, and SFace) using a publicly available face database (Labeled Faces in the Wild) to determine what matches could be found. Our study found that all modules were able to process CT data in addition to T1W and T2W data and that data were successfully anonymized by AFNI's 3Dskullstrip and FSL's BET: they did not match the control image across all facial recognition algorithms. Our study demonstrates the importance of continued vigilance for patient privacy in publicly shared datasets and the importance of evaluation of anonymization methods for CT 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.