摘要
文章以近二十年医学图像分割领域文献为研究对象,基于Cite Space软件对该项技术的研究现状、知识群组、研究主题及其演化路径进行系统研究;并在公开数据集上对同类医学图像分割方法进行对比实验与分析。结果表明,医学图像分割技术研究核心主要表现在计算机科学、生物医学与人工智能等学科,并正在从以区域生长、模糊聚类技术为中心的模式向以深度学习等新兴人工智能技术为中心的模式转变,为产业发展战略决策提供参考依据。
In this paper,the literature in the field of medical image segmentation in the past 20 years is taken as the research object.Based on CiteSpace software,the research status,knowledge group,research topic and evolution path of this technology are systematically studied.Comparative experiment and analysis of similar medical image segmentation methods are carried out on open dataset.The results indicate that the research core of medical image segmentation technology is mainly manifested in disciplines such as computer science,biomedicine,and artificial intelligence,and is transitioning from a mode centered on regional growth and fuzzy clustering technology to a mode centered on emerging artificial intelligence technologies such as deep learning,providing reference basis for industrial development strategic decision-making.
作者
刘良斌
杜宝林
卢琰
王建全
LIU Liangbin;DU Baolin;LU Yan;WANG Jianquan(Guangdong Science&Technology Infrastructure Center,Guangzhou 510033,China;Guangdong Institute of Computing Technology Application,Guangzhou 510033,China;The First People's Hospital of Kashi Prefecture,Kashi 844099,China)
出处
《现代信息科技》
2023年第13期105-111,共7页
Modern Information Technology
基金
广东省援疆科技(特派员)项目(2018YJ003)
广东省重点领域研发计划项目(2020B0101130019)。
