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

2025/06/30 by Wang, Fangyijie, Whelan, Kevin, Balado, Félix +2
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2506.23664

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.

Citations

• FetalFlex: Anatomy-Guided Diffusion Model for Flexible Control on Fetal Ultrasound Image Synthesis
• Generative Diffusion Model Bootstraps Zero-shot Classification of Fetal Ultrasound Images In Underrepresented African Populations
• Micrograph segmentations for DDEVD
• Segment Anything
• Diffusion Models for Medical Image Analysis: A Comprehensive Survey
• Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries
• High-Resolution Image Synthesis with Latent Diffusion Models
• High-Resolution Image Synthesis with Latent Diffusion Models
• LoRA: Low-Rank Adaptation of Large Language Models
• Diffusion Models Beat GANs on Image Synthesis
• Learning Transferable Visual Models From Natural Language Supervision
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• U-Net: Convolutional Networks for Biomedical Image Segmentation
• Adam: A Method for Stochastic Optimization

Related

• Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models
• Towards Realistic Ultrasound Fetal Brain Imaging Synthesis
• Segmenting Fetal Head with Efficient Fine-tuning Strategies in Low-resource Settings: an empirical study with U-Net
• Generative Diffusion Model Bootstraps Zero-shot Classification of Fetal Ultrasound Images In Underrepresented African Populations
• FUSQA: Fetal Ultrasound Segmentation Quality Assessment