标题： 基于扩散模型的胎儿头部超声分割数据增强方法 显示英文标题

标题： Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation

摘要： 医学影像数据由于隐私和监管限制，在其他领域中更难获取。此外，标记需要临床专家进行成本高昂、耗时的手动图像标注。为克服这些挑战，合成医学数据生成提供了一个有前景的解决方案。生成式人工智能（GenAI）采用生成深度学习模型，已被证明在生成逼真的合成图像方面非常有效。本研究提出了一种新颖的基于掩码引导的GenAI方法，使用扩散模型生成与分割掩码配对的合成胎儿头部超声图像。这些合成对用于监督微调分割一切模型（SAM）的真实数据集。我们的结果表明，合成数据有效地捕捉了真实图像特征，并且在使用有限的真实图像-掩码对进行训练时，该方法达到了最先进的胎儿头部分割效果。特别是，使用来自西班牙队列和非洲队列的少量超声图像，分割的Dice分数分别达到94.66%和94.38%。我们的代码、模型和数据可在GitHub上获得。 显示英文摘要

摘要： Medical image data is less accessible than in other domains due to privacy and regulatory constraints. In addition, labeling requires costly, time-intensive manual image annotation by clinical experts. To overcome these challenges, synthetic medical data generation offers a promising solution. Generative AI (GenAI), employing generative deep learning models, has proven effective at producing realistic synthetic images. This study proposes a novel mask-guided GenAI approach using diffusion models to generate synthetic fetal head ultrasound images paired with segmentation masks. These synthetic pairs augment real datasets for supervised fine-tuning of the Segment Anything Model (SAM). Our results show that the synthetic data captures real image features effectively, and this approach reaches state-of-the-art fetal head segmentation, especially when trained with a limited number of real image-mask pairs. In particular, the segmentation reaches Dice Scores of 94.66\% and 94.38\% using a handful of ultrasound images from the Spanish and African cohorts, respectively. Our code, models, and data are available on GitHub.