用扩散模型生成胎儿头超声图像和分割图，提升小样本下的分割精度。
Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
- 用掩码引导的扩散模型生成带标注的合成超声图像。
- 仅用少量真实数据即达94.38%~94.66%的Dice分数。
- 适合医疗数据少、标注难的医学图像分割任务。
由于隐私和监管限制，医学图像数据获取困难，且标注需临床专家耗时人工完成。为此，本研究提出一种基于扩散模型的掩码引导生成方法，合成配对的胎儿头超声图像与分割掩码，用于扩充真实数据集，以监督微调分割一切模型（SAM）。实验表明，合成数据有效捕捉真实图像特征，在有限真实图像对下实现当前最优胎儿头分割性能。在西班牙和非洲队列中，分别达到94.66%和94.38%的Dice分数。代码、模型与数据已开源于GitHub。
原文摘要 · Abstract (English)
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.
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