“分段任何”(SAM)在通用智能超声图像引导中的潜力。

IF 5.7 4区 生物学 Q1 BIOLOGY
Guochen Ning, Hanyin Liang, Zhongliang Jiang, Hui Zhang, Hongen Liao
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引用次数: 3

摘要

超声图像引导是一种经常用于帮助提供护理的方法,它依赖于对信息的准确感知,特别是组织识别,来指导医疗程序。它被广泛用于各种复杂的场景中。最近在大型模型方面的突破,如用于自然语言处理的ChatGPT和用于图像分割的任意分割模型(SAM),已经彻底改变了与信息的交互。这些大型模型展示了对基本信息的革命性理解,为医学带来了希望,包括通用自主超声图像引导的潜力。目前的研究评估了SAM在常用超声图像上的性能,并讨论了SAM对智能图像引导框架的潜在贡献,特别关注自主和通用超声图像引导。结果表明,该方法具有较好的超声图像分割效果,具有实现通用智能超声图像引导的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The potential of 'Segment Anything' (SAM) for universal intelligent ultrasound image guidance.

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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来源期刊
CiteScore
13.60
自引率
1.80%
发文量
47
审稿时长
>12 weeks
期刊介绍: BioScience Trends (Print ISSN 1881-7815, Online ISSN 1881-7823) is an international peer-reviewed journal. BioScience Trends devotes to publishing the latest and most exciting advances in scientific research. Articles cover fields of life science such as biochemistry, molecular biology, clinical research, public health, medical care system, and social science in order to encourage cooperation and exchange among scientists and clinical researchers.
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