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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

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Published on: August 23, 2017

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

Adrien Meyer^1,2, Aditya Murali^3,4, Farahdiba Zarin^3,4

¹University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France. ameyer1@unistra.fr.

International Journal of Computer Assisted Radiology and Surgery

|September 11, 2025

Summary

UltraSam, a new model, effectively analyzes ultrasound images by training on diverse datasets. This approach enhances segmentation and classification across various medical imaging tasks, overcoming data limitations.

Keywords:

Foundation models Large-scale dataset SAM Ultrasound

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Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Automated ultrasound (US) image analysis faces challenges due to complex anatomy and limited annotated data.
Existing pretraining methods struggle with US due to domain shifts and clinical variability.

Purpose of the Study:

To develop UltraSam, a SAM-style model for prompt-conditioned segmentation on diverse US datasets.
To enable generalization to various downstream tasks without unified labels.

Main Methods:

Compiled US-43d, a dataset of 43 open-access US datasets (282,000+ images, 58 structures).
Adapted and fine-tuned SAM, evaluating transferability across tasks.
Proposed prompted classification for improved performance using object-specific prompts and image features.

Main Results:

UltraSam outperformed existing SAM variants in prompt-based segmentation.
UltraSam surpassed self-supervised US foundation models in prompted classification and instance segmentation.

Conclusions:

SAM-style training on diverse, sparsely annotated US data enables task generalization.
UltraSam leverages fragmented public datasets for scalable US representation learning.
Code and pretrained models are released to encourage community contribution.