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ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data
Lyndon Boone1, Mahdi Biparva2, Parisa Mojiri Forooshani2
1Department of Medical Biophysics, University of Toronto, Toronto, Canada; Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Canada; Physical Sciences, Sunnybrook Research Institute, Toronto, Canada.
Neuroimage
|July 26, 2023
Summary
Deep artificial neural networks (DNNs) struggle with MRI data variations. A new platform, ROOD-MRI, benchmarks DNN robustness to distribution shifts and artifacts, revealing susceptibility and guiding improved model design for medical imaging analysis.
Area of Science:
- Medical image analysis
- Neuroimaging
- Artificial intelligence
Background:
- Deep artificial neural networks (DNNs) excel in medical image analysis but are vulnerable to data distribution shifts from varying scanners and protocols.
- Existing neuroimage analysis lacks standardized platforms and accessible datasets for assessing DNN robustness to these real-world variations.
- Magnetic Resonance Imaging (MRI) data variability poses a significant challenge for reliable DNN deployment in clinical settings.
Purpose of the Study:
- To introduce ROOD-MRI, a novel platform for benchmarking the robustness of DNNs to out-of-distribution (OOD) data, corruptions, and artifacts in MRI.
- To provide tools for generating diverse MRI benchmarking datasets simulating real-world distribution shifts.
- To establish new metrics and methodologies for evaluating DNN performance on segmentation tasks under challenging conditions.
Main Methods:
- Developed ROOD-MRI, a flexible platform with modules for dataset generation using MRI-specific transforms and benchmarking metrics for image segmentation.
- Applied the methodology to hippocampus, ventricle, and white matter hyperintensity segmentation tasks across large studies.
- Publicly released the hippocampus dataset as an open benchmark for the research community.
Main Results:
- Evaluated modern DNNs, demonstrating high susceptibility to distribution shifts and corruptions in MRI data.
- Found that data augmentation improves robustness for anatomical segmentation but not for challenging lesion-based tasks.
- Benchmarked U-Nets and vision transformers, identifying architecture-specific vulnerabilities to certain data transforms.
Conclusions:
- DNNs exhibit significant robustness limitations in MRI analysis, particularly with OOD data and artifacts.
- ROOD-MRI provides a crucial open-source framework for developing more robust DNNs for neuroimaging.
- Future research should focus on model design strategies to enhance DNN resilience to diverse MRI data characteristics.
