Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
This addresses data scarcity in medical imaging for clinicians and researchers, though it is incremental as it applies existing generative AI techniques to a specific domain.
The study tackled the problem of limited labeled medical image data for fetal head ultrasound segmentation by proposing a diffusion model-based data augmentation method that generates synthetic image-mask pairs, achieving state-of-the-art segmentation with Dice Scores of 94.66% and 94.38% on Spanish and African cohorts using only a few real images.
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.