{"title":"“分段任何”(SAM)在通用智能超声图像引导中的潜力。","authors":"Guochen Ning, Hanyin Liang, Zhongliang Jiang, Hui Zhang, Hongen Liao","doi":"10.5582/bst.2023.01119","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8957,"journal":{"name":"Bioscience trends","volume":"17 3","pages":"230-233"},"PeriodicalIF":5.7000,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"The potential of 'Segment Anything' (SAM) for universal intelligent ultrasound image guidance.\",\"authors\":\"Guochen Ning, Hanyin Liang, Zhongliang Jiang, Hui Zhang, Hongen Liao\",\"doi\":\"10.5582/bst.2023.01119\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":8957,\"journal\":{\"name\":\"Bioscience trends\",\"volume\":\"17 3\",\"pages\":\"230-233\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2023-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioscience trends\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.5582/bst.2023.01119\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioscience trends","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.5582/bst.2023.01119","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
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.
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.