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Domain-Randomized Deep Learning for Neuroimage Analysis: Selecting Training Strategies, Navigating Challenges, and
1Athinoula A. Martinos Center for Biomedical Imaging and the Departments of Radiology at Harvard Medical School and Massachusetts General Hospital.
IEEE Signal Processing Magazine
|November 27, 2025
Summary
Deep learning models for medical imaging can be improved using synthetic data. This domain-randomization strategy enhances model generalization across various imaging types without retraining.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Neuroscience
Background:
- Deep learning models offer high speed and accuracy in neuroimage analysis.
- Limited training datasets hinder model robustness and generalizability, especially in magnetic resonance imaging (MRI) due to variations in pulse sequences and hardware.
- Existing models struggle with diverse image appearances, necessitating retraining or fine-tuning for new data.
Purpose of the Study:
- To review the principles, implementation, and potential of synthesis-driven training for deep learning models in neuroimage analysis.
- To highlight the benefits of domain randomization for improving model generalization and resistance to overfitting.
- To discuss practical considerations for adopting synthesis-driven training to make deep learning more accessible.
Main Methods:
- Utilizing a domain-randomization strategy to train deep neural networks on synthetic images with randomized intensities and anatomical content.
- Generating diverse training data from anatomical segmentation maps.
- Evaluating the effectiveness of the approach across various imaging modalities (MRI, CT, PET, OCT) and beyond neuroimaging (ultrasound, microscopy, X-ray microtomography).
Main Results:
- The synthesis-driven training paradigm enables models to accurately process unseen image types without retraining or fine-tuning.
- Demonstrated effectiveness across multiple imaging modalities and scientific imaging applications.
- Key benefits include improved generalization and enhanced resistance to overfitting.
Conclusions:
- Synthesis-driven training, particularly domain randomization, significantly enhances the generalizability of deep learning models in medical imaging.
- This approach allows models to adapt to diverse image appearances across different scanners and sequences, reducing the need for extensive retraining.
- The technique holds promise for developing more accessible and robust deep learning tools for domain experts with limited computational resources.
