ArticleEuropean radiology2020
Facing privacy in neuroimaging: removing facial features degrades performance of image analysis methods.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential.Computers in biology and medicine · 2025Article
- Image analysis research in neuroradiology: bridging clinical and technical domains.Neuroradiology · 2025Review
- Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs.AJNR. American journal of neuroradiology · 2025Article
- Removing facial features from structural MRI images biases visual quality assessment.PLoS biology · 2025Article
- Defacing biases visual quality assessments of structural MRI.bioRxiv : the preprint server for biology · 2025Article
- WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis.Scientific data · 2025Article
- Implementation and validation of face de-identification (de-facing) in ADNI4.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- The magic, memory, and curiosity fMRI dataset of people viewing magic tricks.Scientific data · 2024Article
- Assessing the Impact of Defacing Algorithms on Brain Volumetry Accuracy in MRI Analyses.Dementia and neurocognitive disorders · 2024Article
- Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency.Human brain mapping · 2024Article
- Reproducibility evaluation of the effects of MRI defacing on brain segmentation.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- A face-off of MRI research sequences by their need for de-facing.NeuroImage · 2023Article
- Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases.Frontiers in oncology · 2023Article
- Modified MRI Anonymization (De-Facing) for Improved MEG Coregistration.Bioengineering (Basel, Switzerland) · 2022Article
- Advances in human intracranial electroencephalography research, guidelines and good practices.NeuroImage · 2022Review
- Face recognition from research brain PET: An unexpected PET problem.NeuroImage · 2022Article
- Impact of defacing on automated brain atrophy estimation.Insights into imaging · 2022Article
- Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group.Human brain mapping · 2022Review
- Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed.Human brain mapping · 2022Review
- A comparative study between state-of-the-art MRI deidentification and AnonyMI, a new method combining re-identification risk reduction and geometrical preservation.Human brain mapping · 2021Article
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21 authors.
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Abstract
backgroundRecent 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.
methodsFFR 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 the MAGNIMS Study Group, lesion volumes (WMLV) were measured by lesion prediction algorithm in lesion segmentation toolbox. In 84 glioblastoma patients of the PICTURE Study Group, tumor volumes (GBV) were measured by BraTumIA. Failed analyses on FFR-processed images were recorded. Only cases in which all image analyses completed successfully were analyzed. Differences between outcomes obtained from FFR-processed and full images were assessed, by quantifying the intra-class correlation coefficient (ICC) for absolute agreement and by testing for systematic differences using paired t tests.
resultsAutomated analysis methods failed in 0-19% of cases in FFR-processed images versus 0-2% of cases in full images. ICC for absolute agreement ranged from 0.312 (GBV after FaceMasking) to 0.998 (WMLV after Defacing). FaceMasking yielded higher NBV (p = 0.003) and WMLV (p ≤ 0.001). GBV was lower after QuickShear and Defacing (both p < 0.001).
conclusionsAll three outcome measures were affected differently by FFR, including failure of analysis methods and both "random" variation and systematic differences. Further study is warranted to ensure high-quality neuroimaging research while protecting participants' privacy. KEY POINTS: • Protecting participants' privacy when sharing MRI data is important. • Impact of three facial features removal methods on subsequent analysis was assessed in three clinical groups. • Removing facial features degrades performance of image analysis methods.
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