OTLesMix: Wasserstein Barycenter and Optimal Transport Map for Synthetic Lesion Generation with Diverse Shapes and Locations
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TL;DR - OTLesMix is a data-augmentation method that uses Wasserstein barycenters and optimal transport maps to synthesize brain lesions with more varied shapes and locations than existing mix-based augmentation. It matters because lesion segmentation models are data-starved, and this yields measurable Dice gains over training without synthetic data.
- Targets a known limitation of mixing-based augmentation (e.g. sample-combination strategies): synthesized lesions show limited variability in shape and location.
- Uses the Wasserstein barycenter plus the optimal transport plan between real samples to interpolate lesions, producing realistic yet diverse synthetic examples.
- Evaluated on three brain lesion segmentation tasks; improves Dice by 2.9–6.6 points versus a model trained without synthetic data.
- Reported to outperform state-of-the-art mix-based augmentation baselines; no architecture change is implied — the contribution is at the data-synthesis stage.
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OTLesMix: Wasserstein Barycenter and Optimal Transport Map for Synthetic Lesion Generation with Diverse Shapes and Locations
TL;DR - OTLesMix is a data-augmentation method that uses Wasserstein barycenters and optimal transport maps to synthesize brain lesions with more varied shapes and locations than existing mix-based augmentation. It matters because lesion segmentation models are data-starved, and this yields measurable Dice gains over training without synthetic data.
- Targets a known limitation of mixing-based augmentation (e.g. sample-combination strategies): synthesized lesions show limited variability in shape and location.
- Uses the Wasserstein barycenter plus the optimal transport plan between real samples to interpolate lesions, producing realistic yet diverse synthetic examples.
- Evaluated on three brain lesion segmentation tasks; improves Dice by 2.9–6.6 points versus a model trained without synthetic data.
- Reported to outperform state-of-the-art mix-based augmentation baselines; no architecture change is implied — the contribution is at the data-synthesis stage.