Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Background And Purpose: To ensure data privacy, the development of defacing processes, which anonymize brain images by obscuring facial features, is crucial. However, the impact of these defacing methods on brain imaging analysis poses significant concern. This study aimed to evaluate the reliability of three different defacing methods in automated brain volumetry.
Methods: Magnetic resonance imaging with three-dimensional T1 sequences was performed on ten patients diagnosed with subjective cognitive decline. Defacing was executed using mri_deface, BioImage Suite Web-based defacing, and Defacer. Brain volumes were measured employing the QBraVo program and FreeSurfer, assessing intraclass correlation coefficient (ICC) and the mean differences in brain volume measurements between the original and defaced images.
Results: The mean age of the patients was 71.10±6.17 years, with 4 (40.0%) being male. The total intracranial volume, total brain volume, and ventricle volume exhibited high ICCs across the three defacing methods and 2 volumetry analyses. All regional brain volumes showed high ICCs with all three defacing methods. Despite variations among some brain regions, no significant mean differences in regional brain volume were observed between the original and defaced images across all regions.
Conclusions: The three defacing algorithms evaluated did not significantly affect the results of image analysis for the entire brain or specific cerebral regions. These findings suggest that these algorithms can serve as robust methods for defacing in neuroimaging analysis, thereby supporting data anonymization without compromising the integrity of brain volume measurements.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11300685 | PMC |
| http://dx.doi.org/10.12779/dnd.2024.23.3.127 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Analysis of Factors Affecting Quality in Structural Magnetic Resonance Images.
Hum Brain Mapp
August 2025
Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Combining Magnetic Resonance Images (MRI) from different sources is an increasingly common practice that holds high scientific value. Differences in acquisition parameters and participant characteristics can lead to variations in image quality, highlighting the importance of ensuring these variations do not result in biased statistical outcomes. Here, we investigated contributions of both technical and participant-related factors to MRI quality.
View Article and Find Full Text PDFThe climate gluing protests: analyzing their development and framing in media since 1986 using sentiment analyses and frame detection models.
Front Big Data
May 2025
Institute of Human-Centred Computing, Graz University of Technology, Graz, Austria.
Recent climate-related protests by social movements such as , and others have included actions like defacing artwork and gluing oneself to objects and streets. Using sentiment analysis and frame detection models, we analyze a corpus of all available English-language news articles in LexisNexis, with the first recorded instance of a gluing protest appearing in 1986. Our study traces the development of this protest tactic over time and addresses three central questions from social movement literature: the use of glue in protests, the geographical spread of this tactic, and the framing of these actions.
View Article and Find Full Text PDFRemoving facial features from structural MRI images biases visual quality assessment.
PLoS Biol
April 2025
Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
A critical step before data-sharing of human neuroimaging is removing facial features to protect individuals' privacy. However, not only does this process redact identifiable information about individuals, but it also removes non-identifiable information. This introduces undesired variability into downstream analysis and interpretation.
View Article and Find Full Text PDFMedical imaging privacy: A systematic scoping review of key parameters in dataset construction and data protection.
J Med Imaging Radiat Sci
September 2025
Oral Pathology and Microbiology, Centre for Dental Education and Research, All India Institute of Medical Sciences, New Delhi, India. Electronic address:
Background: With digitalization in the field of healthcare, using patient image based data, there is also increasing concerns on protection of patient privacy. Globally various legal rules and regulations have been adopted for stringent measures on data privacy. However, despite the growing importance of privacy, there are currently no universally defined protocols outlining the specific parameters for the de-identification/pseudo-anonymization of medical images.
View Article and Find Full Text PDFAnalyzing the TotalSegmentator for facial feature removal in head CT scans.
Radiography (Lond)
January 2025
Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health, Berlin, Germany.
Background: Facial recognition technology in medical imaging, particularly with head scans, poses privacy risks due to identifiable facial features. This study evaluates the use of facial recognition software in identifying facial features from head CT scans and explores a defacing pipeline using TotalSegmentator to reduce re-identification risks while preserving data integrity for research.
Methods: 1404 high-quality renderings from the UCLH EIT Stroke dataset, both with and without defacing were analysed.