计算机工程与应用2024，Vol.60Issue(17)：1-16,16.DOI:10.3778/j.issn.1002-8331.2401-0136
视觉大模型SAM在医学图像分割中的应用综述
Review of Application of Visual Foundation Model SAM in Medical Image Segmentation
摘要
Abstract
With the continuous development of foundation models technology,visual foundation model represented by the segment anything model(SAM)has made significant breakthroughs in the field of image segmentation.SAM,driven by prompts,accomplishes a series of downstream segmentation tasks,aiming to address all image segmentation issues comprehensively.Therefore,the application of SAM in medical image segmentation is of great significance,as its genera-lization performance can adapt to various medical images,providing healthcare professionals with a more comprehensive understanding of anatomical structures and pathological information.This paper introduces commonly used datasets for image segmentation,provides detailed explanations of SAM's network architecture and generalization capabilities.It focuses on a thorough analysis of SAM's application in five major categories of medical images:whole-slide imaging,magnetic resonance imaging,computed tomography,ultrasound,and multimodal images.The review summarizes the strengths and weaknesses of SAM,along with corresponding improvement methods.Combining current challenges in the field of medical image segmentation,the paper discusses and anticipates future directions for SAM's development.关键词
视觉大模型/分割一切模型(SAM)/医学图像/图像分割Key words
visual foundation model/segment anything model(SAM)/medical images/image segmentation分类
信息技术与安全科学引用本文复制引用
孙兴,蔡肖红,李明,张帅,马金刚..视觉大模型SAM在医学图像分割中的应用综述[J].计算机工程与应用,2024,60(17):1-16,16.基金项目
国家自然科学基金(81973981,82074579) （81973981,82074579）
2022年山东省研究生优质教育教学资源项目(SDYAL2022041). （SDYAL2022041）
