用最优传输生成多样病灶图像，提升脑部病变分割精度
OTLesMix: Wasserstein Barycenter and Optimal Transport Map for Synthetic Lesion Generation with Diverse Shapes and Locations
- 基于沃尔什斯坦均值与最优传输映射合成病灶
- 在3个任务上使Dice得分提升2.9至6.6点
- 适合需要多样化医学图像增强的研究者
过去十年深度学习的发展推动了医学影像分割的革新，使大规模数据中精准提取病理特征成为可能。数据增强是提升模型训练效果的重要手段，包括空间变换、强度调整等简单方法，以及更先进的合成技术。其目标是从现有数据集中生成新且真实的样本以丰富训练集。尽管已有多种混合策略被提出，但其生成的病灶形状和位置多样性仍有限。本文提出一种新图像合成方法OTLesMix，利用沃尔什斯坦均值（Wasserstein barycenter）与最优传输映射（optimal transport plan），生成真实且多样的病灶样本。我们在三个脑部病变分割任务上评估该方法，结果显示，相较于未使用合成数据的模型，其Dice分数提升2.9至6.6点，并优于现有最先进的混合类方法。
原文摘要 · Abstract (English)
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.
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