publication . Article . Other literature type . 2020

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

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.; ...
Open Access English
  • Published: 01 Feb 2020
Abstract
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 impact of three FFR methods on subsequent automated image analysis to obtain clinically relevant outcome measurements in three clinical groups. Methods FFR was performed using QuickShear, FaceMasking, and Defacing. In 110 subjects of Alzheimer’s Disease Neuroimaging Initiative, normalized brain volumes (NBV) were measured by SIENAX. In 70 multiple sclerosis patients of th...
Subjects
free text keywords: /dk/atira/pure/researchoutput/pubmedpublicationtype/D016428, Journal Article, Database, Ethics, Magnetic resonance imaging, Neuroimaging, Privacy, Radiology Nuclear Medicine and imaging, yes, Magnetic Resonance
Funded by
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
Communities
Neuroinformatics

Gkoulalas-Divanis A, Grigorios L (2015) Medical data privacy handbook. Springer Budin F, Zeng D, Ghosh A, Bullitt E (2008) Preventing facial recognition when rendering MR images of the head in three dimensions. Med Image Anal 12:229-239 Prior FW, Brunsden B, Hildebolt C et al (2009) Facial recognition from volume-rendered magnetic resonance imaging data. IEEE Trans Inf Technol Biomed 13:5-9 Parks CL, Monson KL (2017) Automated facial recognition of computed tomography-derived facial images: patient privacy implications. J Digit Imaging 30:204-214 Song X, Wang J, Wang A et al (2015) DeID - a data sharing tool for neuroimaging studies. Front Neurosci 9:325 Langer SG, Shih G, Nagy P, Landman BA (2018) Collaborative and reproducible research: goals, challenges, and strategies. J Digit Imaging 31:275-282 Holmes AJ, Hollinshead MO, O'Keefe TM et al (2015) Brain genomics superstruct project initial data release with structural, functional, and behavioral measures. Sci Data 2:150031 Liew SL, Anglin JM, Banks NW et al (2018) A large, open source dataset of stroke anatomical brain images and manual lesion segmentations. Sci Data 5:180011 Van Essen DC, Smith SM, Barch DM et al (2013) The WU-Minn Human Connectome Project: an overview. Neuroimage 80:62-79 Kushida CA, Nichols DA, Jadrnicek R, Miller R, Walsh JK, Griffin K (2012) Strategies for de-identification and anonymization of electronic health record data for use in multicenter research studies.

Med Care 50(Suppl):S82-S101 Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL (2007) Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J Cogn Neurosci 19: 1498-1507 Schimke N, Hale J (2011) Quickshear defacing for neuroimages.

Proceedings of the 2nd USENIX conference on Health security and privacy USENIX Association Milchenko M, Marcus D (2013) Obscuring surface anatomy in volumetric imaging data. Neuroinformatics 11:65-75 Bischoff-Grethe A, Ozyurt IB, Busa E et al (2007) A technique for the deidentification of structural brain MR images. Hum Brain Mapp 28:892-903

15. Wyman BT, Harvey DJ, Crawford K et al (2013) Standardization of analysis sets for reporting results from ADNI MRI data. Alzheimers Dement 9:332-337

16. de Sitter A, Steenwijk MD, Ruet A et al (2017) Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study. Neuroimage 163:106-114

17. Cover KS, van Schijndel RA, Versteeg A et al (2016) Reproducibility of hippocampal atrophy rates measured with manual, FreeSurfer, AdaBoost, FSL/FIRST and the MAPS-HBSI methods in Alzheimer's disease. Psychiatry Res Neuroimaging 252:26-35

18. Ropele S, Kilsdonk ID, Wattjes MP et al (2014) Determinants of iron accumulation in deep grey matter of multiple sclerosis patients. Mult Scler 20:1692-1698 [OpenAIRE]

19. Kurtzke JF (1983) Rating neurologic impairment in multiple sclerosis: an Expanded Disability Status Scale (EDSS). Neurology 33: 1444-1452 [OpenAIRE]

20. Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17:825-841

21. Cormen TH, Leiserson CE, Rivest RL, Stein C (2007) Introduction to algorithms, second edition. The MIT Press, Cambridge, London

22. Andrew AM (1979) Another efficient algorithm for conex hills in two dimensions. Inf Process Lett 9:216-219

23. Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17:143-155

24. Popescu V, Battaglini M, Hoogstrate WS et al (2012) Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis. Neuroimage 61:1484-1494 Schmidt P (2017) Bayesian inference for structured additive regression models for large-scale problems with applications to medical imaging. PhD thesis, LudwigMaximilians-Universität München.

Available via http://nbn-resolvingde/urn:nbn:de:bvb:19-203731 Bauer S, Fejes T, Slotboom J, Wiest R, Nolte LP, Reyes M (2012) Segmentation of brain tumor images based on integrated hierarchical classification and regularization. Proceedings of MICCAI BraTS Work, pp 10-13 Weir JP (2005) Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res 19:231- 240 Cicchetti DV (1994) Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess 6:284-290 Dice LR (1954) Measures of the amount of ecologic association between species. Ecology 26:297-302 Dadar M, Fonov VS, Collins DL, Alzheimer 's Disease Neuroimaging Initiative (2018) A comparison of publicly available linear MRI stereotaxic registration techniques. Neuroimage 174: 191-200 Abramian D, Eklund A (2018) Refacing: reconstructing anonymized facial features using GANs. arXiv preprint arXiv: 1810.06455 Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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