Removing facial features from structural MRI images biases visual quality assessment.
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 i...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-04-01
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| Series: | PLoS Biology |
| Online Access: | https://doi.org/10.1371/journal.pbio.3003149 |
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| _version_ | 1850129047231660032 |
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| author | Céline Provins Élodie Savary Thomas Sanchez Emeline Mullier Jaime Barranco Elda Fischi-Gómez Yasser Alemán-Gómez Jonas Richiardi Russell A Poldrack Patric Hagmann Oscar Esteban |
| author_facet | Céline Provins Élodie Savary Thomas Sanchez Emeline Mullier Jaime Barranco Elda Fischi-Gómez Yasser Alemán-Gómez Jonas Richiardi Russell A Poldrack Patric Hagmann Oscar Esteban |
| author_sort | Céline Provins |
| collection | DOAJ |
| description | 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. This registered report investigated the degree to which the so-called defacing altered the quality assessment of T1-weighted images of the human brain from the openly available "IXI dataset". The effect of defacing on manual quality assessment was investigated on a single-site subset of the dataset (N = 185). By comparing two linear mixed-effects models, we determined that four trained human raters' perception of quality was significantly influenced by defacing by modeling their ratings on the same set of images in two conditions: "nondefaced" (i.e., preserving facial features) and "defaced". In addition, we investigated these biases on automated quality assessments by applying repeated-measures, multivariate ANOVA (rm-MANOVA) on the image quality metrics extracted with MRIQC on the full IXI dataset (N = 581; three acquisition sites). This study found that defacing altered the quality assessments by humans and showed that MRIQC's quality metrics were mostly insensitive to defacing. |
| format | Article |
| id | doaj-art-05fd09846da247faae8b8bad104db7af |
| institution | OA Journals |
| issn | 1544-9173 1545-7885 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Biology |
| spelling | doaj-art-05fd09846da247faae8b8bad104db7af2025-08-20T02:33:07ZengPublic Library of Science (PLoS)PLoS Biology1544-91731545-78852025-04-01234e300314910.1371/journal.pbio.3003149Removing facial features from structural MRI images biases visual quality assessment.Céline ProvinsÉlodie SavaryThomas SanchezEmeline MullierJaime BarrancoElda Fischi-GómezYasser Alemán-GómezJonas RichiardiRussell A PoldrackPatric HagmannOscar EstebanA 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. This registered report investigated the degree to which the so-called defacing altered the quality assessment of T1-weighted images of the human brain from the openly available "IXI dataset". The effect of defacing on manual quality assessment was investigated on a single-site subset of the dataset (N = 185). By comparing two linear mixed-effects models, we determined that four trained human raters' perception of quality was significantly influenced by defacing by modeling their ratings on the same set of images in two conditions: "nondefaced" (i.e., preserving facial features) and "defaced". In addition, we investigated these biases on automated quality assessments by applying repeated-measures, multivariate ANOVA (rm-MANOVA) on the image quality metrics extracted with MRIQC on the full IXI dataset (N = 581; three acquisition sites). This study found that defacing altered the quality assessments by humans and showed that MRIQC's quality metrics were mostly insensitive to defacing.https://doi.org/10.1371/journal.pbio.3003149 |
| spellingShingle | Céline Provins Élodie Savary Thomas Sanchez Emeline Mullier Jaime Barranco Elda Fischi-Gómez Yasser Alemán-Gómez Jonas Richiardi Russell A Poldrack Patric Hagmann Oscar Esteban Removing facial features from structural MRI images biases visual quality assessment. PLoS Biology |
| title | Removing facial features from structural MRI images biases visual quality assessment. |
| title_full | Removing facial features from structural MRI images biases visual quality assessment. |
| title_fullStr | Removing facial features from structural MRI images biases visual quality assessment. |
| title_full_unstemmed | Removing facial features from structural MRI images biases visual quality assessment. |
| title_short | Removing facial features from structural MRI images biases visual quality assessment. |
| title_sort | removing facial features from structural mri images biases visual quality assessment |
| url | https://doi.org/10.1371/journal.pbio.3003149 |
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