Simulated MRI Artifacts: Testing Machine Learning Failure Modes | JoVE Visualize

Area of Science:

Artificial Intelligence
Medical Imaging
Machine Learning

Background:

Real-world deployment of machine learning (ML) in medicine lags behind research due to insufficient testing data.
A lack of robust test cases, particularly those simulating common errors, hinders the reliability of ML models in clinical settings.
Brain tumor segmentation models require rigorous evaluation against various failure modes to ensure safe and effective clinical application.

Purpose of the Study:

To systematically evaluate the performance of a pretrained machine learning brain tumor segmentation model under simulated magnetic resonance imaging (MRI) artifacts.
To identify specific MRI artifacts that pose the greatest risk of failure for AI-driven medical image analysis.
To assess the model's susceptibility to common acquisition and preprocessing errors in MRI data.

Main Methods:

Seven types of MRI artifacts were simulated, including motion, susceptibility-induced signal loss, aliasing, field inhomogeneity, sequence mislabeling, sequence misalignment, and skull stripping failures.
A pretrained brain tumor segmentation model utilizing standard MRI sequences was subjected to these simulated artifacts.
The model's performance was quantitatively evaluated under these induced 'stress test' conditions.

Main Results:

Sequence mislabeling, a simple artifact, had the most significant negative impact on model performance.
Motion, field inhomogeneity, and sequence misalignment also substantially decreased segmentation accuracy.
The model demonstrated particular vulnerability to artifacts affecting the fluid attenuation inversion recovery (FLAIR) sequence.

Conclusions:

Simulated MRI artifacts provide a valuable method for testing the robustness of brain tumor segmentation models.
This artifact simulation methodology can be extended to evaluate other machine learning models across various medical imaging applications.
Identifying and mitigating failure modes through artifact simulation is crucial for advancing the clinical translation of AI in radiology.