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

TL;DR

This paper introduces a diffusion model-based data augmentation method for fetal head ultrasound segmentation, significantly improving segmentation accuracy with limited real data by generating realistic synthetic images and masks.

Contribution

A novel mask-guided generative AI approach using diffusion models to produce synthetic fetal ultrasound images with segmentation masks for data augmentation.

Findings

Achieved Dice Scores of 94.66% and 94.38% with limited data

Synthetic data effectively captures real image features

State-of-the-art segmentation performance with minimal real data

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.