One of the main challenges in current research on segmentation in cardiac ultrasound is the lack of large and varied labeled datasets and the differences in annotation conventions between datasets. This makes it difficult to design robust segmentation models that generalize well to external datasets. This work utilizes diffusion models to create generative augmentations that can significantly improve diversity of the dataset and thus the generalisability of segmentation models without the need for more annotated data. The augmentations are applied in addition to regular augmentations. A visual test survey showed that experts cannot clearly distinguish between real and fully generated images. Using the proposed generative augmentations, segmentation robustness was increased when training on an internal dataset and testing on an external dataset with an improvement of over 20 millimeters in Hausdorff distance. Additionally, the limits of agreement for automatic ejection fraction estimation improved by up to 20% of absolute ejection fraction value on out of distribution cases. These improvements come exclusively from the increased variation of the training data using the generative augmentations, without modifying the underlying machine learning model. The augmentation tool is available as an open source Python library at https://github.com/GillesVanDeVyver/EchoGAINS.

翻译：当前心脏超声图像分割研究面临的主要挑战之一是缺乏大规模、多样化的标注数据集，以及不同数据集间标注规范的差异。这使得构建能够良好泛化至外部数据集的鲁棒分割模型变得困难。本研究利用扩散模型生成增强数据，可在无需额外标注数据的前提下，显著提升数据集的多样性，从而改善分割模型的泛化能力。此类增强方法可与常规数据增强技术结合使用。视觉测试调查显示，专家无法明确区分真实图像与完全生成的图像。采用所提出的生成式增强方法后，在内部数据集训练、外部数据集测试的场景下，分割鲁棒性得到显著提升——Hausdorff距离改善超过20毫米。此外，在分布外病例中，自动射血分数估计的一致性限度提升了高达绝对射血分数值的20%。这些改进完全源于通过生成式增强技术增加训练数据的多样性，而无需修改底层机器学习模型。该增强工具已作为开源Python库发布于 https://github.com/GillesVanDeVyver/EchoGAINS。