Assessing the Impact of Defacing Algorithms on Brain Volumetry Accuracy in MRI Analyses
10.12779/dnd.2024.23.3.127
- Author:
Dong-Woo RYU
1
;
ChungHwee LEE
;
Hyuk-je LEE
;
Yong S SHIM
;
Yun Jeong HONG
;
Jung Hee CHO
;
Seonggyu KIM
;
Jong-Min LEE
;
Dong Won YANG
Author Information
1. Department of Neurology, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Publication Type:Original Article
- From:Dementia and Neurocognitive Disorders
2024;23(3):127-135
- CountryRepublic of Korea
- Language:English
-
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.
