Code corresponding to finalized Registered Report: 'Removing facial features from structural MRI images biases visual quality assessment'
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2025
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| _version_ | 1866902134787670016 |
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| author | Céline Provins Oscar Esteban |
| author_facet | Céline Provins Oscar Esteban |
| contents | <p>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15101274 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Code corresponding to finalized Registered Report: 'Removing facial features from structural MRI images biases visual quality assessment' Céline Provins Oscar Esteban quality-control MRI anonymization defacing <p>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.</p> |
| title | Code corresponding to finalized Registered Report: 'Removing facial features from structural MRI images biases visual quality assessment' |
| topic | quality-control MRI anonymization defacing |
| url | https://doi.org/10.5281/zenodo.15101274 |