ArticleInsights into imaging2022
Impact of defacing on automated brain atrophy estimation.
Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Deep learning-based synthetic brain MRI for the assessment of regional atrophy patterns in neurodegenerative diseases.European radiology · 2026Article
- Facial feature removal in magnetic resonance imaging scans of adults with Down syndrome: A de-facing methodological study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Analysis of Factors Affecting Quality in Structural Magnetic Resonance Images.Human brain mapping · 2025Article
- Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs.AJNR. American journal of neuroradiology · 2025Article
- Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline.Journal of imaging informatics in medicine · 2025Article
- Article
- Automated quality control of T1-weighted brain MRI scans for clinical research datasets: methods comparison and design of a quality prediction classifier.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- PyFaceWipe: a new defacing tool for almost any MRI contrast.Magma (New York, N.Y.) · 2024Article
- OpenMAP-T1: A Rapid Deep-Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain.Human brain mapping · 2024Article
- Implementation and validation of face de-identification (de-facing) in ADNI4.Alzheimer's & dementia : the journal of the Alzheimer's Association · 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
- Prediction of therapy response of breast cancer patients with machine learning based on clinical data and imaging data derived from breast [European journal of nuclear medicine and molecular imaging · 2024Article
- Bridging Huntington's disease research with big data science: Harmonized neuroimaging datasets from multiple studies.Imaging neuroscience (Cambridge, Mass.) · 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
- Artificial intelligence tools in clinical neuroradiology: essential medico-legal aspects.Neuroradiology · 2023Review
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8 authors.
Funding
Abstract
backgroundDefacing has become mandatory for anonymization of brain MRI scans; however, concerns regarding data integrity were raised. Thus, we systematically evaluated the effect of different defacing procedures on automated brain atrophy estimation.
methodsIn total, 268 Alzheimer's disease patients were included from ADNI, which included unaccelerated (n = 154), within-session unaccelerated repeat (n = 67) and accelerated 3D T1 imaging (n = 114). Atrophy maps were computed using the open-source software veganbagel for every original, unmodified scan and after defacing using afni_refacer, fsl_deface, mri_deface, mri_reface, PyDeface or spm_deface, and the root-mean-square error (RMSE) between z-scores was calculated. RMSE values derived from unaccelerated and unaccelerated repeat imaging served as a benchmark. Outliers were defined as RMSE > 75th percentile and by using Grubbs's test.
resultsBenchmark RMSE was 0.28 ± 0.1 (range 0.12-0.58, 75th percentile 0.33). Outliers were found for unaccelerated and accelerated T1 imaging using the 75th percentile cutoff: afni_refacer (unaccelerated: 18, accelerated: 16), fsl_deface (unaccelerated: 4, accelerated: 18), mri_deface (unaccelerated: 0, accelerated: 15), mri_reface (unaccelerated: 0, accelerated: 2) and spm_deface (unaccelerated: 0, accelerated: 7). PyDeface performed best with no outliers (unaccelerated mean RMSE 0.08 ± 0.05, accelerated mean RMSE 0.07 ± 0.05). The following outliers were found according to Grubbs's test: afni_refacer (unaccelerated: 16, accelerated: 13), fsl_deface (unaccelerated: 10, accelerated: 21), mri_deface (unaccelerated: 7, accelerated: 20), mri_reface (unaccelerated: 7, accelerated: 6), PyDeface (unaccelerated: 5, accelerated: 8) and spm_deface (unaccelerated: 10, accelerated: 12).
conclusionMost defacing approaches have an impact on atrophy estimation, especially in accelerated 3D T1 imaging. Only PyDeface showed good results with negligible impact on atrophy estimation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.