Back to all papers

Ultrasam: a foundation model for ultrasound using large open-access segmentation datasets.

Authors

Meyer A,Murali A,Zarin F,Mutter D,Padoy N

Affiliations (5)

  • University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France. [email protected].
  • IHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France. [email protected].
  • University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France.
  • IHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France.
  • Hôpitaux Universitaires de Strasbourg, Strasbourg, France.

Abstract

Automated ultrasound (US) image analysis remains a longstanding challenge due to anatomical complexity and the scarcity of annotated data. Although large-scale pretraining has improved data efficiency in many visual domains, its impact in US is limited by a pronounced domain shift from other imaging modalities and high variability across clinical applications, such as chest, ovarian, and endoscopic imaging. To address this, we propose UltraSam, a SAM-style model trained on a heterogeneous collection of publicly available segmentation datasets, originally developed in isolation. UltraSam is trained under the prompt-conditioned segmentation paradigm, which eliminates the need for unified labels and enables generalization to a broad range of downstream tasks. We compile US-43d, a large-scale collection of 43 open-access US datasets comprising over 282,000 images with segmentation masks covering 58 anatomical structures. We explore adaptation and fine-tuning strategies for SAM and systematically evaluate transferability across downstream tasks, comparing against state-of-the-art pretraining methods. We further propose prompted classification, a new use case where object-specific prompts and image features are jointly decoded to improve classification performance. In experiments on three diverse public US datasets, UltraSam outperforms existing SAM variants on prompt-based segmentation and surpasses self-supervised US foundation models on downstream (prompted) classification and instance segmentation tasks. UltraSam demonstrates that SAM-style training on diverse, sparsely annotated US data enables effective generalization across tasks. By unlocking the value of fragmented public datasets, our approach lays the foundation for scalable, real-world US representation learning. We release our code and pretrained models at https://github.com/CAMMA-public/UltraSam and invite the community to further this effort by continuing to contribute high-quality datasets.

Topics

Journal Article

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.