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

• URL: http://arxiv.org/abs/2506.23664v2
• Date: Thu, 10 Jul 2025 10:51:46 GMT
• Title: Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
• Authors: Fangyijie Wang, Kevin Whelan, Félix Balado, Kathleen M. Curran, Guénolé Silvestre,
• Abstract summary: Generative AI (GenAI) has proven effective at producing realistic synthetic images.<n>This study proposes a novel mask-guided GenAI approach to generate synthetic fetal head ultrasound images.<n>Our results show that the synthetic data captures real image features effectively.
• Score: 1.188383832081829
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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