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基于模式识别的生物医学图像处理研究现状

徐莹莹 沈红斌

徐莹莹, 沈红斌. 基于模式识别的生物医学图像处理研究现状[J]. 电子与信息学报, 2020, 42(1): 201-213. doi: 10.11999/JEIT190657
引用本文: 徐莹莹, 沈红斌. 基于模式识别的生物医学图像处理研究现状[J]. 电子与信息学报, 2020, 42(1): 201-213. doi: 10.11999/JEIT190657
Yingying XU, Hongbin SHEN. Review of Research on Biomedical Image Processing Based on Pattern Recognition[J]. Journal of Electronics and Information Technology, 2020, 42(1): 201-213. doi: 10.11999/JEIT190657
Citation: Yingying XU, Hongbin SHEN. Review of Research on Biomedical Image Processing Based on Pattern Recognition[J]. Journal of Electronics and Information Technology, 2020, 42(1): 201-213. doi: 10.11999/JEIT190657

基于模式识别的生物医学图像处理研究现状

doi: 10.11999/JEIT190657
基金项目: 国家自然科学基金(61803196, 61671288),广东省自然科学基金(2018030310282)
详细信息
    作者简介:

    徐莹莹:女,1989年生,副教授,研究方向为生物图像信息学与模式识别

    沈红斌:男,1979年生,教授,研究方向为模式识别、数据挖掘以及生物信息学

    通讯作者:

    沈红斌 hbshen@sjtu.edu.cn

  • 中图分类号: TN911.73

Review of Research on Biomedical Image Processing Based on Pattern Recognition

Funds: The National Natural Science Foundation of China (61803196, 61671288), The Natural Science Foundation of Guangdong Province (2018030310282)
  • 摘要: 海量的生物医学图像蕴含着丰富的信息,模式识别算法能够从中挖掘规律并指导生物医学基础研究和临床应用。近年来,模式识别和机器学习理论和实践不断完善,尤其是深度学习的广泛研究和应用,促使人工智能、模式识别与生物医学的交叉研究成为了当前的前沿热点,相关的生物医学图像研究有了突破式的进展。该文首先简述模式识别的常用算法,然后总结了这些算法应用于荧光显微图像、组织病理图像、医疗影像等多种图像中的挑战性和国内外研究现状,最后对几个潜在研究方向进行了分析和展望。
  • 图  1  多种生物医学图像的示例及在临床和研究中的主要应用

    图  2  传统模式识别方法处理生物医学图像的一般步骤

    图  3  经典卷积神经网络模型的时间轴及特点

    图  4  荧光点的两种建模方法示意图

    图  5  当前生物医学图像研究中的主要挑战和可行解决方向

    表  1  常用的生物图像数据集

    类型数据集数据量特点
    荧光显微图像CYCLoPs[23]超3×105幅蛋白荧光图像标注酵母细胞中蛋白质16类亚细胞位置及表达量
    HPA IF[24]2.2×105幅IF图像20余个细胞系的蛋白图像,标注34类亚细胞位置
    2D HeLa[25]862幅荧光显微图像HeLa宫颈癌细胞系,标注10个标志蛋白的表达模式
    2D CHO[26]327幅荧光显微图像中国仓鼠卵巢细胞图像,标注5个标志蛋白的表达模式
    组织病理图像BreakHis[27]7909幅H&E图像乳腺良性和恶性肿瘤图像,共8类病理状态
    TCGA[28]18462幅H&E图像记录36类癌症的病理检查及治疗数据,
    TMAD[29]3726幅IHC图像对蛋白质着色的评分,分为4个等级
    HPA IHC[30]约106幅IHC图像人体正常和癌症组织的蛋白图像,标注3类亚细胞位置
    医疗影像图像BRATS[31]65幅MRI图像经专家人工分割的脑胶质瘤患者的多对比度MR扫描图像,两组癌症分级
    ADNI[32]2000余名志愿者的MRI、PET图像阿尔茨海默病患者和健康组对照
    ISLES[33]103位病人的MRI图像缺血性中风病人图像,由专家人工分割出损伤的脑组织
    DeepLesion[34]32735幅CT图像肾脏病变、骨病变、肺结节、淋巴结肿大等多种病理诊断
    下载: 导出CSV

    表  2  常用的生物图像处理工具

    类型处理工具作用
    通用ImageJ[36]对多种生物医学图像做如缩放、旋转、平滑、区域分割、像素统计等多种处理分析
    CellProfiler[37]分割荧光点或细胞,提取细胞的统计学特征
    荧光显微图像Squassh[38]分割和定量亚细胞结构
    DeepLoc[39]基于荧光图像预测蛋白质的亚细胞位置
    CellOrganizer[40]对多种细胞亚结构建立生成式模型,产生新的细胞图像或视频
    OMERO.searcher[41]图像匹配和检索
    组织病理图像HistomicsML[42]交互式机器学习系统,训练基于病理图像的分类器
    IHC Profiler[43]IHC图像统计学特征提取,着色评分
    iLocator[44-46]基于IHC图像的蛋白质亚细胞位置预测系统
    医疗影像图像RayPlus[47]在线的云端的智能医学影像平台,集成3维影像重建、专科影像分析等功能
    Mimics[48]一套高度整合而且易用的3D图像生成及编辑处理软件
    ANTS[49]提供了高级的工具用于大脑图像配准映射,在解释和可视化多维数据方面有优势
    FSL[50]用于分析fMRI,MRI和DTI大脑成像数据的综合软件库
    下载: 导出CSV
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    • 收稿日期:  2019-08-29
    • 修回日期:  2019-11-12
    • 网络出版日期:  2019-11-18
    • 刊出日期:  2020-01-21

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