Reproducibility evaluation of the effects of MRI defacing on brain segmentation

Chenyu Gao; Bennett A. Landman; Jerry L. Prince; Aaron Carass

doi:10.1117/1.JMI.10.6.064001

8 November 2023 Reproducibility evaluation of the effects of MRI defacing on brain segmentation

Chenyu Gao, Bennett A. Landman, Jerry L. Prince, Aaron Carass

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 6, 064001 (November 2023). https://doi.org/10.1117/1.JMI.10.6.064001

Abstract

Purpose

Recent advances in magnetic resonance (MR) scanner quality and the rapidly improving nature of facial recognition software have necessitated the introduction of MR defacing algorithms to protect patient privacy. As a result, there are a number of MR defacing algorithms available to the neuroimaging community, with several appearing in just the last 5 years. While some qualities of these defacing algorithms, such as patient identifiability, have been explored in the previous works, the potential impact of defacing on neuroimage processing has yet to be explored.

Approach

We qualitatively evaluate eight MR defacing algorithms on 179 subjects from the OASIS-3 cohort and 21 subjects from the Kirby-21 dataset. We also evaluate the effects of defacing on two neuroimaging pipelines—SLANT and FreeSurfer—by comparing the segmentation consistency between the original and defaced images.

Results

Defacing can alter brain segmentation and even lead to catastrophic failures, which are more frequent with some algorithms, such as Quickshear, MRI_Deface, and FSL_deface. Compared to FreeSurfer, SLANT is less affected by defacing. On outputs that pass the quality check, the effects of defacing are less pronounced than those of rescanning, as measured by the Dice similarity coefficient.

Conclusions

The effects of defacing are noticeable and should not be disregarded. Extra attention, in particular, should be paid to the possibility of catastrophic failures. It is crucial to adopt a robust defacing algorithm and perform a thorough quality check before releasing defaced datasets. To improve the reliability of analysis in scenarios involving defaced MRIs, it is encouraged to include multiple brain segmentation pipelines.

Citation Download Citation

Chenyu Gao, Bennett A. Landman, Jerry L. Prince, and Aaron Carass "Reproducibility evaluation of the effects of MRI defacing on brain segmentation," Journal of Medical Imaging 10(6), 064001 (8 November 2023). https://doi.org/10.1117/1.JMI.10.6.064001

Received: 18 May 2023; Accepted: 24 October 2023; Published: 8 November 2023

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KEYWORDS

Image segmentation

Magnetic resonance imaging

Brain

Neuroimaging

Reproducibility

Detection and tracking algorithms

Image processing

Purpose

Approach

Results

Conclusions

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