Facing privacy in neuroimaging: removing facial features degrades performance of image analysis methods

Background Recent studies have created awareness that facial features can be reconstructed from high-resolution MRI. Therefore, data sharing in neuroimaging requires special attention to protect participants’ privacy. Facial features removal (FFR) could alleviate these concerns. We assessed the impa...

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Bibliographic Details
Published in:	European radiology 2020-02, Vol.30 (2), p.1062-1074
Main Authors:	de Sitter, A., Visser, M., Brouwer, I., Cover, K. S., van Schijndel, R. A., Eijgelaar, R. S., Müller, D. M. J., Ropele, S., Kappos, L., Rovira, Á., Filippi, M., Enzinger, C., Frederiksen, J., Ciccarelli, O., Guttmann, C. R. G., Wattjes, M. P., Witte, M. G., de Witt Hamer, P. C., Barkhof, F., Vrenken, H.
Format:	Article
Language:	English
Subjects:	Algorithms Alzheimer's disease Automation Correlation coefficient Correlation coefficients Data retrieval Diagnostic Radiology Failure analysis Glioblastoma Image analysis Image processing Image segmentation Imaging Internal Medicine Interventional Radiology Lesions Magnetic Resonance Magnetic resonance imaging Medical ethics Medical imaging Medicine Medicine & Public Health Multiple sclerosis Neurodegenerative diseases Neuroimaging Neuroradiology Performance degradation Privacy Radiology Test procedures Ultrasound
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