OTLesMix用Wasserstein重心造形状位置更多样的病灶，脑分割Dice提升2.9-6.6，比现有mix方法都强。
OTLesMix是一种面向医学影像的数据增强方法，利用Wasserstein重心和最优传输计划生成新样本。研究者在三个脑病灶分割任务上测试，与不使用合成数据的模型相比，Dice分数提升2.9到6.6个点。该方法在生成病灶的形状和位置多样性上优于现有基于混合的方法。
OTLesMix: Wasserstein Barycenter and Optimal Transport Map for Synthetic Lesion Generation with Diverse Shapes and Locations
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.