Domain-Randomized Deep Learning for Neuroimage Analysis: Selecting Training Strategies, Navigating Challenges, and...

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.