Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
Journal:
arXiv
Published Date:
Jun 30, 2025
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.
