Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913935593046016 |
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| author | Wang, Fangyijie Whelan, Kevin Balado, Félix Curran, Kathleen M. Silvestre, Guénolé |
| author_facet | Wang, Fangyijie Whelan, Kevin Balado, Félix Curran, Kathleen M. Silvestre, Guénolé |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_23664 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation Wang, Fangyijie Whelan, Kevin Balado, Félix Curran, Kathleen M. Silvestre, Guénolé Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.23664 |