DRIPS: Domain Randomisation for Image-based Perivascular spaces Segmentation.

Area of Science:

• Neuroimaging
• Medical Image Analysis
• Computational Neuroscience

Background:

• Perivascular spaces (PVS) are crucial imaging biomarkers for brain health.
• Accurate PVS segmentation is challenging due to modality-specific methods and the need for manual labels.

Purpose of the Study:

• To introduce DRIPS (Domain Randomisation for Image-based PVS Segmentation), a physics-inspired framework for automated PVS segmentation.
• To generate synthetic brain images and labels for on-the-fly deep learning training, enhancing generalization.

Main Methods:

• DRIPS integrates anatomical and shape priors with physics-based image generation.
• Synthetic data generation includes resampling, geometric/intensity transformations, and simulated artifacts.
• Evaluated on diverse MRI cohorts (N=165) and a 3D ex vivo brain model using AUPRC and DSC.

Main Results:

• DRIPS and Frangi achieved above-chance AUPRC across all datasets.
• DRIPS and nnU-Net showed comparable performance on isotropic data, outperforming others.
• DRIPS significantly outperformed all competitors on anisotropic data.

Conclusions:

• DRIPS provides accurate, automated PVS segmentation across heterogeneous imaging settings.
• The framework reduces the need for manual segmentation and modality-specific models.
• DRIPS offers a robust solution for PVS analysis in brain health research.