Assessing the Impact of Defacing Algorithms on Brain Volumetry Accuracy in MRI Analyses
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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.
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.
참고문헌 (15)
- Schwarz CG, "A face-off of MRI research sequences by their need for de-facing", Neuroimage, 276, pp.120199~ (2023)
- Gao C, "Reproducibility evaluation of the effects of MRI defacing on brain segmentation", J Med Imaging(Bellingham), 10, pp.064001~ (2023)
- Rubbert C, "Impact of defacing on automated brain atrophy estimation", Insights Imaging, 13, pp.54~ (2022)
- Bhalerao GV, "Systematic evaluation of the impact of defacing on quality and volumetric assessments on T1-weighted MR-images", J Neuroradiol, 49, pp.250~257 (2022)
- Buimer EEL, "De-identification procedures for magnetic resonance images and the impact on structural brain measures at different ages", Hum Brain Mapp, 42, pp.3643~3655 (2021)
- de Sitter A, "Facing privacy in neuroimaging : removing facial features degrades performance of image analysis methods", Eur Radiol, 30, pp.1062~1074 (2020)
- Jessen F, "A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease", Alzheimers Dement, 10, pp.844~852 (2014)
- Bischoff-Grethe A, "A technique for the deidentification of structural brain MR images", Hum Brain Mapp, 28, pp.892~903 (2007)
- Jeong YU, "De-identification of facial features in magnetic resonance images : software development using deep learning technology", J Med Internet Res, 22, pp.e22739~ (2020)
- Ryu DW, "Automated brain volumetric program measuring regional brain atrophy in diagnosis of mild cognitive impairment and Alzheimer’s disease dementia", Brain Imaging Behav, 16, pp.2086~2096 (2022)
- Theyers AE, "Multisite comparison of MRI defacing software across multiple cohorts", Front Psychiatry, 12, pp.617997~ (2021)
- Schwarz CG, "Face recognition from research brain PET : an unexpected PET problem", Neuroimage, 258, pp.119357~ (2022)
- Schwarz CG, "Effects of de-facing software mri_reface on utility of imaging biomarkers used in Alzheimer’s disease research", Neuroimage Clin, 40, pp.103507~ (2023)
- Bruña R, "Modified MRI anonymization(de-facing)for improved MEG coregistration", Bioengineering(Basel), 9, pp.591~ (2022)
- Delbarre DJ, "Application of a convolutional neural network to the quality control of MRI defacing", Comput Biol Med, 151, pp.106211~ (2022)