基于深度学习的急性缺血性脑卒中病灶分割与检测综述  

Survey on Deep Learning-based Lesion Segmentation and Detection in Acute Ischemic Stroke

作　　者：毛天驰 李杨[1] 李明[1] 孙兴 马金刚[1] MAO Tian-Chi;LI Yang;LI Ming;SUN Xing;MA Jin-Gang(College of Intelligence and Information Engineering,Shandong University of Traditional Chinese Medicine,Jinan 250355,China)

机构地区：[1]山东中医药大学智能与信息工程学院,济南250355

出　　处：《计算机系统应用》2025年第1期11-25,共15页Computer Systems & Applications

基　　金：国家自然科学基金(81973981,82074579);2022年山东省研究生优质教育教学资源项目(SDYAL2022041)。

摘　　要：急性缺血性脑卒中是临床上最常见的卒中类型,因其症状突发且治疗时间窗较短等特点,成为全球导致残疾和死亡的重要因素之一.随着人工智能领域的迅速发展,深度学习技术在急性缺血性脑卒中的诊疗中展现出巨大的潜力.深度学习模型能够快速高效地根据患者脑部图像对病灶进行分割与检测.本文介绍深度学习模型的发展历程和用于脑卒中研究的常用公开数据集.针对计算机断层扫描(computerized tomography,CT)和磁共振成像(magnetic resonance imaging,MRI)衍生出的多种模态和扫描序列,详细阐述了深度学习技术在急性缺血性脑卒中病灶分割与检测领域的研究进展,总结并分析了相关研究的改进思路.最后,指出了深度学习在该领域现存的挑战并提出了可能的解决方案.Acute ischemic stroke is the most common type of stroke in clinical practice.Due to its sudden onset and short treatment time window,it becomes one of the important factors leading to disability and death world wide.With the rapid development of artificial intelligence,deep learning technology shows great potential in the diagnosis and treatment of acute ischemic stroke.Deep learning models can quickly and efficiently segment and detect lesions based on patients’brain images.This study introduces the development history of deep learning models and commonly used public datasets for stroke research.For various modalities and scanning sequences derived from computerized tomography(CT)and magnetic resonance imaging(MRI),it elaborates on the research progress of deep learning technology in the field of lesion segmentation and detection in acute ischemic stroke and summarizes and analyzes the improvement ideas of related research.Finally,it points out existing challenges of deep learning in this field and proposes possible solutions.

关 键 词：脑卒中 医学图像分割 计算机视觉 深度学习 计算机断层扫描 磁共振成像 

分 类 号：R743.3[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]