ArXiv

OTLesMix is a synthetic lesion-generation method that uses Wasserstein…

2026-08-06 · 16:54 UTC ·Robin Trombetta, Carole Lartizien ·1 min read
Authors
Robin Trombetta, Carole Lartizien
Categories
cs.CV, cs.LG, eess.IV
arXiv
https://arxiv.org/abs/2608.06264v1
PDF
https://arxiv.org/pdf/2608.06264v1

Brief

OTLesMix leverages Wasserstein barycenters and optimal transport plans to generate synthetic brain lesions with varied shapes and locations, aiming to increase augmentation diversity. Evaluated on three lesion-segmentation tasks, it yields 2.9–6.6 point Dice improvements over no-synthesis baselines and surpasses prior mix-based synthesis methods (Trombetta & Lartizien, arXiv:2608.06264v1, 2026-08-06).

Why it matters

OTLesMix is a synthetic lesion-generation method that uses Wasserstein barycenters and the optimal transport plan to produce realistic, diverse lesion shapes and locations (authors Robin Trombetta and Carole Lartizien; arXiv:2608.06264v1; published 2026-08-06).

Key details

  • On three brain lesion segmentation tasks, OTLesMix increases Dice scores by 2.9 to 6.6 percentage points versus training without synthetic data and outperforms state-of-the-art mix-based augmentation methods.
Source evidence

Abstract

The development of deep learning over the past decade has revolutionized medical imaging segmentation, allowing the extraction of precise descriptors from large volumes to characterize pathologies. Data augmentation is a technique widely regarded as a way to improve model training. It includes simple transformations like spatial operations or intensity modifications, but also more advanced synthesis techniques. Their goal is to generate new realistic samples from an existing dataset to diversify the images used during training. Among them, several propose different mixing strategies to combine real samples. However, one of their major shortcomings is to yield limited variability in terms of generated lesion shapes and locations. In this work, we introduce a novel image synthesis method, called OTLesMix, that leverages Wasserstein barycenter and optimal transport plan to generate realistic and diverse samples. We evaluated our method on three brain lesion segmentation tasks, on which it improves the Dice score compared to a model trained without synthetic data by 2.9 to 6.6 points, and outperforms state-of-the-art mix-based methods.