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基于乳腺X线摄影的肿块检测综述

王俊茜 徐勇 孙利雷 蒲祖辉

王俊茜, 徐勇, 孙利雷, 蒲祖辉. 基于乳腺X线摄影的肿块检测综述.自动化学报, 2021, 47(4): 747-764 doi: 10.16383/j.aas.c180662
引用本文: 王俊茜, 徐勇, 孙利雷, 蒲祖辉. 基于乳腺X线摄影的肿块检测综述.自动化学报, 2021, 47(4): 747-764 doi: 10.16383/j.aas.c180662
Wang Jun-Qian, Xu Yong, Sun Li-Lei, Pu Zu-Hui. Survey of mass detection based on mammography. Acta Automatica Sinica, 2021, 47(4): 747-764 doi: 10.16383/j.aas.c180662
Citation: Wang Jun-Qian, Xu Yong, Sun Li-Lei, Pu Zu-Hui. Survey of mass detection based on mammography. Acta Automatica Sinica, 2021, 47(4): 747-764 doi: 10.16383/j.aas.c180662

基于乳腺X线摄影的肿块检测综述

doi: 10.16383/j.aas.c180662
基金项目: 

深圳市科技创新委员会 GJHZ20180419190732022

贵州省公共大数据重点实验室开放课题基金 2018BDKFJJ001

详细信息
    作者简介:

    王俊茜  哈尔滨工业大学(深圳)计算机科学与技术学院生物计算研究中心博士研究生. 2018年获得哈尔滨工业大学计算机科学与技术专业工学硕士学位. 主要研究方向为模式识别, 计算机视觉, 深度学习与医学图像处理.E-mail: wangjunqian@stu.hit.edu.cn

    孙利雷  贵州大学计算机科学与技术学院博士研究生. 2016年获得贵州大学计算机技术专业工程硕士学位. 主要研究方向为模式识别, 医学图像处理.E-mail: sunlileisun@163.com

    蒲祖辉  临床医学博士, 深圳市第二人民医院(深圳大学第一附属医院)放射科副主任医师.主要研究方向为肿瘤学, 循证医学, 人工智能. E-mail: pupeter190@163.com

    通讯作者:

    徐勇  哈尔滨工业大学(深圳)计算机科学与技术学院生物计算研究中心教授. 2005年获得南京理工大学模式识别与智能系统专业博士学位. 研究方向为模式识别, 计算机视觉, 深度学习, 生物特征识别, 医学图像处理与生物信息学.本文通信作者.E-mail: laterfall@hit.edu.cn

Survey of Mass Detection Based on Mammography

Funds: 

Shenzhen Municipal Science and Technology Innovation Council GJHZ20180419190732022

the Foundation of Guizhou Provincial Key Laboratory of Public Big Data 2018BDKFJJ001

More Information
    Author Bio:

    WANG Jun-Qian  Ph. D. candidate at Bio-Computing Research Center, College of Computer Science and Technology, Harbin Institute of Technology, Shenzhen. She received her master degree in computer science and technology from Harbin Institute of Technology in 2018. Her research interest covers pattern recognition, computer vision, deep learning, and biomedical image processing

    SUN Li-Lei  Ph. D. candidate at the College of Computer Science and Technology, Guizhou University. He received his master degree in computer technology from Guizhou University in 2016. His research interest covers pattern recognition and biomedical image processing

    PU Zu-Hui  M. D., associate chief physician in Radiology Department, Shenzhen Second People's Hospital (The First Affiliated Hospital of Shenzhen University). His research interest covers oncology, evidence-based medicine, and artificial intelligence

    Corresponding author: XU Yong  Professor at the Bio-Computing Research Center, College of Computer Science and Technology, Harbin Institute of Technology, Shenzhen. He received his Ph. D. degree in pattern recognition and intellectual system from Nanjing University of Science and Technology in 2005. His research covers pattern recognition, computer vision, deep learning, biometric identification technology, biomedical image processing, and bioinformatics. Corresponding author of this paper
  • 摘要: 早期筛查和及时治疗是控制乳腺癌死亡率最为有效的方法.乳腺X线摄影检查作为医学界公认的最有效的早期乳腺癌筛检工具, 可以很好地反映出乳腺存在的异常情况.在临床应用中, 乳腺癌的X线摄影直接征象为钙化和肿块, 对乳腺X线摄影中钙化点的检测技术已经相当的成熟, 但对肿块区域的检测和分类依旧是一项具有挑战性的任务. 因此, 本文对近几年提出的基于全乳腺X线摄影的肿块检测方法进行简要综述, 分别从基于传统的乳腺肿块检测与分割方法和基于深度学习的乳腺肿块检测方法进行介绍, 并讨论了乳腺X线摄影中肿块检测未来研究的发展趋势.
    Recommended by Associate Editor HUANG Qing-Ming
    1)  本文责任编委 黄庆明
  • 图  1  乳腺肿块((a)~(c)肿块呈现的不同形状, (d)~(f)肿块呈现的不同边界)

    Fig.  1  Breast mass ((a)~(c) mass presents the different shapes, (d)~(f) mass presents the different margins)

    图  2  乳腺肿块检测的计算机辅助检测与诊断框架

    Fig.  2  Framework of computer aided detection and diagnosis for mass detection in mammography

    图  3  RPN结构[48]

    Fig.  3  RPN structure[48]

    图  4  Faster R-CNN框架实现乳腺肿块的检测和分类

    Fig.  4  Faster R-CNN framework for detection and classification of mass in mammography

    图  5  YOLO框架实现乳腺肿块的检测和分类[72-73]

    Fig.  5  YOLO framework for detection and classification of mass in mammography[72-73]

    图  6  STL结构实现乳腺肿块的定位任务[78]

    Fig.  6  STL for localization of mass in mammography[78]

    图  7  FCN框架实现乳腺肿块的检测与分割

    Fig.  7  FCN framework for detection and segmentation of mass in mammography

    图  8  弹性变形实例[79]

    Fig.  8  Effects of performing elastic deformation on a mammography[79]

    图  9  U-Net框架实现乳腺肿块的检测与分割

    Fig.  9  U-Net framework for detection and segmentation of mass in mammography

    图  10  FROC示意图

    Fig.  10  The illustration of FROC

    表  1  基于乳腺X线摄影相关研究的综述论文简介

    Table  1  The introduction of review papers based on mammography

    综述文献 综述的主要内容 发表时间
    Vyborny等[6] 回顾了早期基于乳腺X线摄影的CAD技术发展, 包括图像预处理、乳腺钙化检测、乳腺肿块检测以及乳腺异常状态的良恶性分类.文中从形态学、滤波、模板匹配等方面对乳腺肿块检测方法进行了介绍. 1994年
    Cheng等[26] 对乳腺钙化CAD系统中图像增强、钙化检测与分割以及良恶性分类分别进行了介绍. 2003年
    Cheng等[27] 对乳腺肿块CAD系统中图像增强、乳腺肿块检测与分割以及良恶性分类分别进行了介绍.文中从阈值分割、统计学方法、区域增长、聚类、边缘检测等具体算法对乳腺肿块的检测与分割进行详细介绍. 2006年
    Rangayyan等[28] 从乳腺图像的对比度增强、乳腺钙化检测和分析、乳腺肿块检测和分析、双侧乳腺的不对称分析和乳腺结构扭曲检测5个方面对乳腺X线摄影CAD技术进行较为全面的综述. 文中着重对解决针状和星状病变的肿块检测问题的方法进行总结. 2007年
    Elter等[8] 回顾了基于乳腺X线摄影的乳腺钙化和肿块的分割与分类技术.其中, 作者将乳腺肿块的检测与分割方法分为区域增长、轮廓、水平集和动态规划等算法. 2009年
    Oliver等[29] 针对乳腺肿块检测与分割的研究进行综述, 分别从单视图和多视图两个角度进行概述.其中, 对基于单视图的乳腺肿块检测方法介绍分为无监督的检测方法(基于区域、轮廓和聚类的目标分割方法)和有监督的检测方法(基于模型的目标分割方法)两种, 对文献进行科学有效的归类. 2010年
    He等[30] 总结了基于乳腺X线摄影的乳腺密度和乳腺实质组织自动分割方法, 并利用分割结果对乳腺密度进行分类, 从而进一步对患乳腺癌的风险进行评估.文中并未对于乳腺异常(如: 肿块或钙化等)分析方法进行介绍. 2015年
    Hamidinekoo等[12] 对近几年提出的基于深度学习方法实现乳腺X线摄影中乳腺肿块和钙化分析以及乳腺组织病理学图像分析的研究进展进行综述.文中只简要介绍基于深度学习实现的乳腺肿块检测方法, 未进行详细介绍与方法归类. 2018年
    下载: 导出CSV

    表  2  基于传统的乳腺肿块检测与分割方法总结

    Table  2  Summary of traditional methods for mass detection and segmentation in mammography

    方法名称 解决问题 优点 缺点
    基于图像增强 乳腺肿块呈现的形态各异, 直接提取图像的低层特征难以区分肿块与正常组织 可以提高肿块与乳腺正常组织对比度, 是提升乳腺肿块区域的检测与分割效果的关键 适用于具有一定显著性特征的乳腺肿块检测任务, 需与传统经典检测与分割算法相结合
    基于阈值分割 经典的图像分割方法, 广泛应用于医学图像分析 适用于肿块、肿瘤性疾病特点; 计算简单; 运算效率高 只考虑像素本身的灰度值, 缺乏对空间特征的考虑
    基于模板匹配 人工设计的检测模型泛化能力很有限, 对特征形态各异的肿块没有很好的鲁棒性特征表示 思路和实现方法相对简单, 不需要设计复杂的特征提取方案, 也不需要对乳腺组织进行细致分割 需要特征丰富的组织模板; 计算成本高、时间开销大
    基于特定医学理论知识的形态学特征模型 有效利用乳腺肿块的相关临床观察特点和诊断经验, 提出对乳腺肿块形态特征假设 将医学知识和计算机技术有效结合, 更科学、更准确地提出目标病灶处理方案 模型设计通常具有高复杂性的多参数特点; 选择和优化模型中参数存在困难
    组合分割 利用不同分割技术, 弥补各种分割方法自身局限性 结合多种特征可以更好地拟合乳腺肿块的特点 算法的复杂度往往很高, 还易出现过度检测的情况
    下载: 导出CSV

    表  3  基于深度学习的乳腺肿块检测方法总结

    Table  3  Summary of deep learning methods for mass detection in mammography

    方法名称 解决问题 优点 缺点
    基于候选框的乳腺肿块检测 传统的乳腺肿块检测与分割方法局限于浅层特征, 缺少鲁棒性表达 利用候选框方法提取ROI, 结合深度学习特征提取, 在目标检测精度上具有优势 忽视引入全局信息, 使易混淆的目标区分性较差; 检测速度慢
    基于回归的乳腺肿块检测 基于候选框的乳腺肿块检测效率慢, 缺少图像全局信息, 不利于医学图像病灶的检测 将检测任务转化为回归问题, 加快了检测速度; 引入图像的全局信息, 降低假阳性检测比例 对乳腺X线摄影中小肿块目标不敏感, 易漏检
    基于弱监督学习的乳腺肿块定位 医学图像需要专业医生标注肿块真实的ROI, 获取大量标注的医学数据存在困难 训练数据仅为图像中目标类别的注释信息, 不需要具有精准的ROI标注, 减少医学图像数据标注成本 网络结构设计、模型训练以及目标函数优化的难度增加
    基于全卷积网络的乳腺肿块检测 对乳腺X线摄影数据进行下采样调整图像大小, 将不利于捕获小肿块特征, 易出现漏检 与输入数据尺寸大小无关, 适合处理全尺寸乳腺X线摄影数据, 更易检测出细小的肿块目标 存储开销大; 计算效率低下
    下载: 导出CSV

    表  4  常用乳腺X线摄影数据集及相关参数

    Table  4  Popular mammography datasets and their relevant parameters

    数据集名称 病例数量 图片数量 视角 异常种类 BI-RADS 标注方式 发布时间
    DDSM[83] 2 605 10 420 MLO, CC 所有种类 ROI轮廓点 1999年
    MIAS[84] 161 322 MLO 所有种类 ROI中心和半径 2003年
    BancoWeb 320 1 473 MLO, CC 所有种类 只有部分数据 2010年
    LAPIMO [85] 标注ROI
    INBreast[86] 115 410 MLO, CC 肿块、钙化、扭曲、不对称 ROI轮廓点 2011年
    BCDR-FM[87] 1 010 3 703 MLO, CC 所有种类 医生观察到的病灶轮廓、异常和预计算图像描述符 2012年
    BCDR-DM 724 3 612
    下载: 导出CSV

    表  5  乳腺X线摄影数据集上乳腺肿块检测方法的性能对比

    Table  5  Comparison of state-of-the-art mass detection methods on mammography datasets

    数据集 方法 传统/深度 TPR FPI 发表时间
    DDSM Dhungel等[60] 深度 75 % 4.8 2015年
    70 % 4
    Al-masni等[72] 深度 99 % 0.22 2018年
    Wu等[70] 深度 81 % 1.1 2018年
    Castro等[79] 深度 60 % 1.864 2018年
    Min等[45] 传统 77 % 3.97 2017年
    Wang等[43] 传统 94 % 2.11 2018年
    INBreast Dhungel等[60] 深度 96${\pm}$3 % 1.2 2015年
    87${\pm}$14 % 0.8
    Akselrod-Ballin等[63] 深度 93 % 0.56 2017年
    Wu等[70] 深度 88 % 0.75 2018年
    Castro等[79] 深度 80 % 0.912 2018年
    Kozegar等[35] 传统 87 % 3.67 2013年
    Min等[45] 传统 94 % 1.99 2017年
    下载: 导出CSV
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