The Potential of “Segment Anything” (SAM) for Universal Intelligent Ultrasound Image Guidance
Open accessAbstract
Ultrasound image guidance is a method often used to help provide care, and it relies on accurate perception of information, and particularly tissue recognition, to guide medical procedures. It is widely used in various scenarios that are often complex. Recent breakthroughs in large models, such as ChatGPT for natural language processing and Segment Anything Model (SAM) for image segmentation, have revolutionized interaction with information. These large models exhibit a revolutionized understanding of basic information, holding promise for medicine, including the potential for universal autonomous ultrasound image guidance. The current study evaluated the performance of SAM on commonly used ultrasound images and it discusses SAM's potential contribution to an intelligent image-guided framework, with a specific focus on autonomous and universal ultrasound image guidance. Results indicate that SAM performs well in ultrasound image segmentation and has the potential to enable universal intelligent ultrasound image guidance.
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ChatGPT can provide initial observations that highlight regions of interest and assist radiologists in their analyses.
“It has the ability to provide initial observations, which can highlight regions of interest and assist radiologists in their analyses [13,25,26,27,28,29,30,31,32,33].”
ChatGPT has the ability to analyse and generate descriptions for medical images, which can enhance the workflow of radiologists.
“The potential of ChatGPT lies in its ability to analyse and generate descriptions for medical images, which can enhance the workflow of radiologists.”
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- Authors
- Ning, G., Liang, H., Jiang, Z., Zhang, H., & Liao, H.
- Affiliations
- Tsinghua University (CN)
- Technical University of Munich (DE)
- Venue
- BioScience Trends
- Publisher
- International Research and Cooperation Association for Bio & Socio-Sciences Advancement (IRCA-BSSA)
- Published
- 2023-06-30
- Volume
- 17
- Issue
- 3
- Pages
- 230-233
- ISSN
- 1881-7815 (ISSN-L)
- 1881-7823
- OpenAlex
- W4381547384
- Cited by
- 17
- References
- 21
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 8 · 2023: 1
- FWCI
- 1.54
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- BioScience Trends (publishedVersion)
- PubMed (publishedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- Funders
- National Natural Science Foundation of China (2022YFC2405200, U22A2051, 82027807, U22A2051, 82027807)
- Tsinghua University (2022ZLB001)
- National Key Research and Development Program of China (2022YFC- 2405200, U22A2051)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- Segment Anything Model, ultrasound guidance, image segmentation, large language models, tissue recognition
- MeSH terms
- Algorithms, Ultrasonography
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