“Face-to-Face”: Open Secularism and the Politics of Display in Quebec’s Bouchard-Taylor Commission

Cox, Jordana

doi:10.1057/978-1-137-49608-9_7

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…Original CT and MRI DICOM files were converted to NIfTI as the anonymization algorithms used for our study only process NIfTI files. The converted NIfTI data were then anonymized using algorithms from AFNI (Analysis of Functional NeuroImages [NIH]: deface, reface, and 3Dskullstrip) and FSL (FMRIB Software Library [Oxford]: BET) 10 , 28 – 32 Anonymization algorithms were used to mask or remove identifiable facial features from the CT or MRI data.…”

Section: Methodsmentioning

confidence: 99%

Anonymization and validation of three-dimensional volumetric renderings of computed tomography data using commercially available T1-weighted magnetic resonance imaging-based algorithms

Patel,

Provenzano,

Loew

2023

J. Med. Imag.

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Previous 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 T1weighted MRI data.Approach: 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 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.Results: Our 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. Conclusions:The 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.

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