深度学习在脑小血管病影像学标志物中的研究进展  

Progress of deep learning in cerebral small vessel disease imaging markers

作　　者：白雪冬[1] 张小雷 夏爽 BAI Xue-dong;ZHANG Xiao-lei;XIA Shuang(Department of Radiology,Affiliated Hospital of Chengde Medical University,Chengde 067000,Hebei,China;Department of Biomedical and Engineering,Chengde Medical University,Chengde 067000,Hebei,China;Department of Radiology,Tianjin First Central Hospital,School of Medicine,Nankai University,Tianjin 300192,China)

机构地区：[1]承德医学院附属医院放射科,067000 [2]承德医学院生物医学工程系,067000 [3]南开大学附属第一中心医院放射科,300192

出　　处：《中国现代神经疾病杂志》2023年第1期9-14,共6页Chinese Journal of Contemporary Neurology and Neurosurgery

基　　金：国家自然科学基金资助项目(项目编号:82171916);河北省卫生健康委重点科技研究计划项目(项目编号:20200385)。

摘　　要：随着人工智能技术的飞速发展,特别是深度学习算法的应用,使脑小血管病典型影像学标志物的检测及量化评估速度增快、准确性提高。本文拟综述深度学习算法在脑微出血、脑白质高信号、扩大的血管周围间隙、腔隙、近期皮质下梗死及脑萎缩等脑小血管病影像学标志物中的研究进展,以为脑小血管病的精准医疗提供支持。With the rapid development of artificial intelligence(AI) technology, especially the application of deep learning(DL), the detection and quantitative evaluation of typical imaging markers of small cerebral vascular disease(CSVD) has been accelerated and the accuracy has been improved. In recent years, it has attracted much attention in the field of medical imaging. This paper intends to summarize the research progress and problems of deep learning in the imaging markers of CSVD such as cerebral microbleeds(CMBs), white matter hyperintensities(WMH), enlarged perivascular space(EPVS),lacunes, recent small subcortical infarcts(RSSI) and cerebral atrophy, so as to provide support for the precise treatment of CSVD.

关 键 词：大脑小血管疾病 深度学习 综述 

分 类 号：R743[医药卫生—神经病学与精神病学] TP18[医药卫生—临床医学] TP391.41[自动化与计算机技术—控制理论与控制工程]