计算机辅助检测/诊断(computer-aided detection/diagnosis,简称CAD)能够提高诊断的准确性,减少假阳性的产生,为医生提供有效的诊断决策支持.旨在分析计算机辅助诊断工具的最新发展.以CAD研究较多的四大致命性癌症的发病医学部位为主线,按照不同的成像技术和病类,对目前CAD在不同医学图像领域的应用进行了较为详尽的综述,从图像数据集、算法和评估方法等方面做多维度梳理.最后分析了医学图像CAD系统研究领域目前存在的问题,并对此领域的研究趋势和发展方向进行展望.
Computer aided detection/diagnosis (CAD) can improve the accuracy of diagnosis, reduce false positive, and provide decision supports for doctors.The main purpose of this paper is to analyze the latest development of computer aided diagnosis tools.Focusing on the top four fatal cancer's incidence positions, major recent publications on CAD applications in different medical imaging areas are reviewed in this survey according to different imaging techniques and diseases.Further more, multidimentional analysis is made on the researches from image data sets, algorithms and evaluation methods.Finally, existing problems, research trend and development direction in the field of medical image CAD system are discussed.
医学影像(medical imaging)是指为了医疗或医学研究, 对人体或人体某部分, 以非侵入方式取得内部组织影像的技术与处理过程.随着各种科学技术的迅速发展, 医学影像技术也有了飞速的进步[
1963年, Lodwick等人发表了把X光片数字化的方法[
本文第1节对医学图像CAD系统进行概述.第2节~第5节分别从肺、乳腺、结直肠、前列腺这4部分, 按照不同的成像技术和病类对近几年的CAD系统进行梳理.第6节回顾医学图像CAD系统的性能评估方法.第7节汇总文中提到的CAD系统所用的主要算法和去假阳性或诊断分类时用到的特征.第8节对此研究领域面临的问题进行描述, 并对研究前景进行展望.第9节对本文进行总结.
基于医学图像的CAD系统分为两类:一类是计算机辅助检测(CADe)系统, 在医学图像上检测异常并定位呈现出来; 另一类是计算机辅助诊断(CADx)系统, 在医学图像上检测异常并帮助医生决定异常的类别以及恶性级别.CADe系统与CADx系统的一般处理框架如
CAD系统流程图
Workflow of computer aided detection/diagnosis systems
从
● 图像获取是指系统获取医学图像的方式, 一般有3种方式:(1)从自建影像库获取, 这些库一般是用从合作医院得到的医学图像建立的[
● 预处理过程是指矫正由于介质衰减、噪声或运动伪影而导致的失真, 对原始图像做归一化处理[
● 为了减少外围组织或背景对感兴趣区域检测的干扰, 减少计算量, 预处理之后, 有些CAD系统还要进行一步图像分割操作, 把欲研究区域从背景或周围组织中分离出来.图像分割是这一步也是CAD系统的基础, 也是图像处理中最重要的步骤之一[
● ROI检测是依据密度、形状等特征把所有疑似病变的区域抽取出来.抽取的信息包括了ROI的位置和形状等特征, 把ROI从周围的组织中标记出来, 所以有时也叫ROI分割.
● 特征提取是利用算法计算ROI的各种特征值, 如形状特征、视觉特征和密度特征等.
● 特征维数较多时要对特征做优化选择, 只保留对分类结果作用大的特征, 即特征选择.
● CADe与CADx的区别是:CADe系统在这个环节把检测到的疑似区域标注出来呈现给医生做诊断[
X光片是最早应用于医疗诊断的影像技术, 现在仍是最常用的检查方式之一.Nagata等人[
(1) 首先用主动轮廓模型在胸片图像上分割出肺, 然后检测出肺的顶点、中线、长和宽, 与已有的模板通过参照点进行配准, 归一化.
(2) 用模板匹配方法检测初始的结节候选区域, 把归一化的肺部图像用7×7个64×64像素的矩阵化分开, 对每一个小区域通过阈值法检测ROI.
(3) 对ROIs进行两个阶段分类, 去除假阳性:第1阶段先用图像局部增强对比法分割出候选结节区域, 用密度梯度计算检测出结节; 第2阶段用多尺度模板匹配技术进一步对检测到的结节做假阳性去除.
该算法取得了良好的实验结果, Nagata等人还发现, 如果分类时考虑更多的特征, 系统性能会得到一步提升.
Htike等人[
秦菊等人[
Harrison等人[
由于成像对象的厚度和肺域的重叠, 侧面视图通常在对比度和信噪比方面都表现出较差的图像质量.因此, 找出侧面胸部的肺部轮廓是很困难的.侧位肺部X光片能够提供用于肺气肿识别的重要信息, 例如, 仅在侧位视图中才能看到后胸部空间的扩张, 它也能准确地描述隔膜扁平化, 然而结合侧位信息的计算机诊断研究却很少. Coppini等人[
Cao等人[
X光成像是一种光影投影, 只显示1个方向上的物体结构, 体内不同器官会发生重叠, 显示不清楚.CT计算多角度的投影, 将这些信息合成横截面图像.CT图像的分辨率是X线的10倍~20倍, 能够分辨尺度较小的组织病变.CT影像是一系列连续的断层图片, 在此基础上可以对肺部进行3D建模, 比CXR更适于肺部疾病诊断.目前, 肺部疾病的计算机辅助检测/诊断研究大都集中在CT图像上进行.
目前, CT检查是肺癌筛查最有效的手段, 越早发现, 病变就越小, 治愈率也就越高[
肺结节
Pulmonary nodules
下面按第2节所归纳的CAD系统的一般处理框架, 对现有基于肺部CT图像的CAD系统进行介绍.
(1) 预处理
CT图像相对于X光片本身, 其噪声已经很少, 但为了得到更精确的结果, 也用一些算法对图像做预处理, 给CAD后面的操作提供更好的输入.Messay等人[
(2) 分割
肺部CT图像上还包含背景、肺壁、心脏和肝等其他器官区域, 在图像预处理阶段要确定肺叶范围, 抽取肺实质(也叫肺实质分割).肺实质分割在肺部疾病诊断中是最为关键的步骤之一[
(3) ROI检测
ROI检测的目的是查找肺叶中的肺结节, 如果有, 则确定其位置[
虽然3D CNN是对时空数据进行统计建模很有前景的工具, 但是它们有一个限制是需要详细的3D标签, 与获得2D标签相比, 这是非常昂贵的.现有的CAD方法依赖于获得大量肺结节的详细标签来训练模型, 这也是困难和耗时的.为了减轻这一挑战, Anirudh等人[
(4) 特征提取
要判断ROIs是不是结节、是良性病变还是恶性结节, CAD系统一般是根据有经验医生标注的样本库训练算法模型, 从形状、纹理、灰度和形态学等角度在候选区域上提取特征值, 利用这些特征值区分不同类别相近的对象.磨玻璃密度影(GGO)结节59%~73%是恶性, 实性结节是恶性的概率是7%~9%, 外形不规则、分叶征和毛刺征的多为恶性, 外形圆形匀滑的多为良性[
医学图像诊断领域尽管有各种优秀的算法出现, 但传统方法建立的特征抽取和模型选择总难鲁棒地泛化.卷积神经网络(CNN)的应用使这一问题得到了很好的解决, CNN基于人工神经网络, 针对不同分类任务, 通过反向传播算法自动强化或削减相应特征的权值, 在卷积层用滤波器自动抽取特征, 实现图像特征提取[
对于胸部CT肺结节诊断分类的问题, 与传统的主要依赖于结节分割进行区域分析的研究不同, Wajid等人[
(5) 特征选择
特征选择是指选择使分类结果最显著的特征子集的过程[
特征的高维不相关、特征子集的异构性以及样本类别分布不均衡, 一直是肺结节检测准确率的阻碍.Cao等人[
纹理特征具有很强的表征力和特异性.Krewer等人[
(6) 去假阳性
检测的结果中, 有一部分得到的ROIs不是结节, 可能是表现相近的有大曲率或分叉的厚血管、呼吸或心脏运动及肺实质组织上的散射形成的斑迹等.上述非结节ROIs也叫假阳性(false positive, 简称FP)ROIs, 需要去除, 为此需要对得到的ROIs提取特征.常用的有密度[
Camarlinghi等人[
Wang等人[
(7) 分类
在CAD系统中, 常用的分类算法有基于规则的分类器、LDA、模式匹配、神经网络(NN)、马尔科夫随机场、SVM[
基于逐像素的深度学习方法如CNN, 由于能够避免由对复杂和细微对象的特征计算或分割不准确而导致的计算错误, 显示出了良好的发展前景[
针对其他系统假阳性率太高的情况, Tajbakhsh等人[
乳腺钼靶摄影(mammography)是一类用低剂量X-ray检查乳腺的技术, 具有全面、直观、操作简单、安全和费用低廉等特点, 是目前诊断早期乳腺癌的首选而有效的方法[
左上图为头尾位右侧, 右上图为头尾位左侧, 左下图为内外侧斜位右侧, 右下图为内外侧斜位左侧
Top left is CranioCaudal (CC) right side, top right is CC left side, bottom left is MedioLateral-Oblique (MLO) right side, bottom right is MLO left side
乳腺钼靶图像在产生时会有内在的噪声, 在正常的腺体和恶性的组织之间, X射线的衰减差较小[
此环节是找到并标出包含异常病变的ROIs.乳腺异常表现为肿块、微钙化、结构扭曲或双边非对称.
(1) 恶性肿块
恶性肿块的征象通常是边缘模糊、毛刺状、分叶状、边界不规则、密度不均匀、灶状致密影等.检测/分割算法常用的有区域增长法[
Dhungel等人[
(2) 微钙化
微钙化是钙在乳腺组织中的沉淀, 通常比周围组织更亮、更小.良性病变的钙化一般是较大的粗糙且边缘较圆和光滑, 恶性钙化表现为许多、聚集、小、大小和形状变化多样、有角、不规则和多向分叉[
Oliver等人构建了一种基于知识的检测微钙化和簇的方法.首先建造一个字典, 字典中的元素是通过一组滤波器得到的包含不同微钙化特征的卷积块.这个字典允许表征已知微钙化的范例, 后面用于表征未知的图像.用有正负样本的微钙化字典训练Gentleboost分类器.最后, 用得到的分类器对未知的乳房X光片检测.3D图像包含更丰富的信息, 对提高医学图像CAD性能有很大的潜力.
Sahiner等人[
乳房组织的致密性和乳房X线照片图像较差的对比度, 是有效识别微钙化的最大障碍, Malar等人[
通过回顾性分析, 谭婉嫦等人[
(3) 结构扭曲
结构扭曲是指乳腺结构变化没有明确的肿瘤发现, 但乳腺组织结构紊乱, 这包括有毛刺从一点辐射出来和病灶收缩或腺体实质的边缘变形.结构扭曲也可以是一个附属的发现, 是第3类最常见的未扪及肿块的乳腺钼靶影像症状的癌症[
尽管是高度恶性, 乳房X线片中的结构扭曲也一直是放射科医师最容易漏检的病变.为了提高检测结构变形的准确率, Yoshikawa等人[
Singh等人[
(4) 双边非对称
双边非对称表示左、右乳腺的一侧和另外一侧对应区域对比, 没有明显的肿块, 表现出一个较大的体积或密度不同, 或更突显的导管[
人工免疫方法是模仿自然免疫系统的一种智能方法, 有耐噪声、无师学习、自组织、记忆等进化学习机理.自适应人工免疫网络是人工免疫网络的一种半监督学习框架, 这种自我智能的网络可用来解决复杂的分类问题, Magna等人[
在二维乳腺投影X片上准确地分割病变区域非常困难, 而且不可靠, Kelder等人[
Wang等人[
Murakami等人[
从乳腺异常ROIs中能够抽取到的特征可达几百维之多, 其中有一些特征与诊断分类无关[
Choi等人[
深度学习模型直接从数据中自动学习特征, 并在自然场景分类和物体检测等计算机视觉问题上取得了显著的成绩[
在乳腺CAD系统中, SVM依然是使用最多的分类方法[
Cascio等人[
受人工免疫算法的启发, Peng等人[
近几年, 深度学习模型在计算机视觉和机器学习中产生了相当有竞争力的结果[
尽管乳腺钼靶影像目前被认为是早期检查乳腺癌最可靠的方式, 能够减少18%~30%的致死率, 但其对比度不高, 肿瘤与周围正常组织的分界不明显, 这会导致10%~30%的癌症漏检[
超声乳腺病变CAD系统一般包括4个环节:图像预处理、图像分割、特征提取/选择和分类.散斑干涉和低对比度是超声影像的主要局限.与其他CAD系统相比, 超声乳腺病变CAD系统的预处理主要致力于增强对比度和抑制散斑.散斑降噪技术主要有滤波方法、小波域法和组合方法这3类[
Moon等人[
Alam等人[
Huang等人[
Cheng等人[
由于乳腺钼靶摄影对病变的显示存在非显著性和误报问题, 这会导致不必要的阴性穿刺活检.锥束乳腺CT扫描仪(bCT)作为一种新的专用影像方法, 能够生成高质量的层析数据以提高乳腺组织和结构的可视化, 并使病灶显示更显著.量化成像分析能够被用来从bCT图像集中抽取关于感兴趣组织的有用的数字信息[
CAD的准确输出依赖于对病灶的有效分割, Kuo等人[
Ray等人[
自20世纪90年代以来, 乳腺MRI就开始被用于表征和检测乳腺病变.MRI的敏感度非常高, 可达到78%~ 98%, 但其特异度不足, 只有43%~75%.近些年发展起来的计算机辅助诊断软件的目的主要是为了使MRI分析和报告更加便利, 或试图进一步突出显示检测到的病变[
Wang等人[
Pang等人[
Gallego-Ortiz等人[
结直肠癌(CRC)是世界范围内第3类最常见癌, 致死率第四, 每年导致700 000人死亡[
结肠息肉
Colon polyp
Kominami等人[
Devi等人[
临床医学光学结直肠镜是目前息肉检测和去除的主要工具, 不过, 由于它有侵入性, 对于50岁以上的人群筛查, 它是被禁止使用的.作为替代, CT结直肠成像(CTC)技术逐渐发展起来, 对息肉的检测展现出较好的性能[
对CT图像的准确和自动的分割, 是CT结肠造影中许多临床应用的关键步骤, 在结肠息肉计算机辅助诊断检测中也是如此.结肠分割实际上是指结肠壁的分割, 是CAD方案的基本步骤.结肠壁的理想分割, 对于整个CAD方案的性能表现至关重要.如果结肠部分错过或小肠和一些其他结肠结构被错误地包括在内, 那么后面的息肉检测和分类的质量将受到明显的影响[
Yang等人[
息肉是从大肠(结肠或直肠)的粘膜突出并伸到肠道(管腔)中的组织的异常生长.一些息肉是扁平的, 其他的有蒂.大部分结肠癌是从息肉发展而来的, 不过这需要5年~15年的恶性转变.结肠息肉的大小是一个与恶性风险相关的生物标志物, 也对其临床处理措施起着指导作用.虚拟结肠镜工作组的专家的一致共识是, 6mm~10mm的阈值是尤其值得关注的[
Chen等人[
Song等人[
张国鹏等人[
在文献[
CAD方案中一般在候选区域检测之后是有监督的分类[
并不是所有提取到的特征都有助于判别病变与非病变, 因此, 在有效分类器的设计中, 选择最具差异性的特征来区分病变与非病变是至关重要的.Xu等人[
对于诊断结肠息肉病变的恶性/良性的CAD, Hu等人[
自体荧光是指生物组织被一定波长的激发光激发后, 处于激发态的分子在下降到基态过程中, 以光量子的形式释放出所吸收的能量, 即荧光.荧光的产生与生物体特定的分子结构有关, 不同的生物组织由于其分子结构的不同, 其所对应的荧光光谱也不同.这种特异性的自体荧光可作为内镜下确定活检部位的参照指标, 有助于提高早期癌症的检出率.
Aihara等人[
前列腺癌(PCa, 如
前列腺癌
Prostatic cancer
减少前列腺癌致死率的主要方法是早期发现并加强治疗.数字直肠检查(DRE)只能鉴别到后周围区域的肿瘤, 因此不能检测到发病于前部周边、中心区域和过渡区域的许多肿瘤[
磁共振(MR)能够提供功能性的组织信息以及解剖信息.多参数核磁技术的T1加权(T1-weighted)、T2加权(T2-weighted)、弥散加权(diffusion-weighted)、动态对比增强(dynamic contrast-enhanced)是目前用在前列腺诊断的常用影像方法[
在基于MRI的CAD系统流程中, 预处理环节的主要目的是减少噪声, 去除伪影和标准化信号密度; 由于后面的操作都集中在前列腺上, 所以需要把前列腺从每一个MRI模式中分割出来; 配准是把所有分割出来的MRI图像匹配重叠到同一个参考帧上, 以消除由于病人移动或不同获取参数引起的不对齐问题; 经过上面的步骤数据正则化以后, 就可以抽取特征并分类这些数据, 得到可能病变的位置(CADe)或这些病变的恶性程度(CADx)[
多参数MRI(MP-MRI)不但能够通过T2-w提供图像的形态学信息, 还能利用DW-MR图像和DCE-MR图像估计组织的生理学特性, 所以逐渐成为潜在的前列腺癌替代筛选方法.Giannini等人[
(1) DWI-T2w配准.首先, 应用刚性配准来校正主要由运动或涡流引起的可能的平移和缩放伪像.对膀胱通过应用于ADC图上的分水岭算法在DW上自动分割, 并且通过
(2) DCE-T2w配准.选择基于相互信息(MI)的相似性作为度量, 使用0阶(boxcar)B样条内核计算图像概率密度函数(PDF); 同时, 为确保平滑度, 用三阶B样条核计算运动图像强度PDF, 通过试错逐渐改进迭代计算, 直到满足终止条件为止.配准之后的数据集中的每一个像素都可以表示成由图像密度、量化的生理信息值和表观扩散系数(ADC)值等特征组成的向量表示.接着, 所有这些参数被送到分类器中, 这个分类器最大化对真阳性的检测, 同时使假阳性良性区域最小化.
Liu等人[
前列腺医学图像分割是临床和图像处理工作流程中的重要一步.在临床设置中, 前列腺分割用于诸如放射治疗、前列腺特异性抗原(PSA)密度的计算, 也是前列腺体积和计算诊断上的指标.在图像处理中, 器官的分割通常是强制性的第1步, 这使得后续的算法可以集中在感兴趣的区域上.这也会降低算法复杂性和计算时间.Litjens等人[
活动外观模型(AAM)使用一组解剖标志来定义每个对象的形状, 然而解剖标志可能难以识别, 而且传统的AAM只允许对单个感兴趣的对象进行分割.MLA可以同时分割多个对象, 并利用多个级别而不是解剖标记定义形状.Toth等人[
高效、准确地提取前列腺, 特别是其临床3D MR图像上的子区域, 对图像引导的前列腺干预和前列腺癌的诊断非常有意义.Qiu等人[
Andrik等人[
对于MRI图像的计算机辅助诊断, Zhou等人[
在计算机辅助诊断领域, 深度学习已经显示了巨大的潜力.但在许多应用中, 大型数据集不可用, 这使得复杂的深度学习神经网络(DNN)的训练困难.Chen等人[
成功治疗前列腺癌在很大程度上取决于早期诊断, 而其确诊要通过手动分析活检样本来确定[
Doyle等人[
性能评估是CAD系统研究的一个重要环节, 使用公共的医学影像样本数据集是各类CAD系统性能有效和客观、公平地评估的基础[
公开的医学影像数据集
Open available medical image databases
数据库名 | 全称 | 影像类别 | 样本量 | 创建组织 | 存储地址 |
ACRIN- CTC | American College of Radiology Imaging Network CT Colonography | 结肠CT | 825个病例 | American College of Radiology Imaging Network | https://wiki.cancerimagingarchive.net/display/Public/CT+COLONOGRAPHY]]> |
DDSM | Digital Database for Screening Mammography | 乳腺钼靶 | 2 620个病例分为43个卷 | University of South Florida | http://marathon.csee.usf.edu/Mammography/Database.html]]> |
JSRT | Japanese Society of Radiological Technology | 肺部X光片 | 247张摄片 | Japanese Society of Radiological Technology | http://www.jsrt.or.jp/jsrt-db/eng.php]]> |
LIDC | Lung Image Database Consortium | 肺部CT, CR, DX | 1 010个病例 | Cancer imaging program | https://imaging.cancer.gov/informatics/lidcidri]]> |
LISS | Lung CT Imaging SignS | 肺部CT | 271病例 | Beijing Institute of Technology | http://www.iscbit.org/LISS.html]]> |
Mini- MIAS | The mini-MIAS database of mammograms | 乳腺钼靶 | 322个病例 | Mammographic Image Analysis Society (MIAS) | http://peipa.essex.ac.uk/info/mias.html]]> |
PFMP | Prostate Fused- MRI-Pathology | 前列腺MR | 28个病例 | Case Western Reserve University, Hospital at the University of Pennsylvania | https://pathology.cancerimagingarchive.net/pathdata/]]> |
PROSTATE- DIAGNOSIS | prostate Magnetic Resonance Images | 前列腺MR | 92个病例 | National Cancer Institute | https://wiki.cancerimagingarchive.net/display/Public/PROSTATE-DIAGNOSIS]]> |
PROMISE12 | Prostate MR Image Segmentation’- challenge 2012 | 前列腺MR | 50个病例 | MICCAI Workshop Organizers | https://promise12.grand-challenge.org/Download/]]> |
RIDER Breast MRI | The Reference Image Database to Evaluate Therapy Response Breast MRI | 乳腺MR | 2 400张摄片 | University of Michigan | https://wiki.nci.nih.gov/display/CIP/RIDER]]> |
RIDER Lung CT | The Reference Image Database to Evaluate Therapy Response Lung CT | 肺部CT, PT | 269 522张摄片 | University of Washington | https://wiki.nci.nih.gov/display/CIP/RIDER]]> |
SCR | Segmentation in Chest Radiographs database | 肺部X光片 | 247张摄片 | Utrecht University | http://www.isi.uu.nl/Research/Databases/SCR/download.php]]> |
通常用敏感度(sensitivity)、特异度(specificity)[
诊断性能评价指标对照表
Comparison table of performance evaluation metrics
真实值 | 输出合计 | ||
CAD输出值 | 真阳性(TP) 假阳性(FP) |
||
真值个数合计 |
敏感度(sensitivity)、特异度(specificity)、假阳性率(FPR)、准确率(accuracy)、精确度(precision)的计算公式分别表达为
敏感度(sensitivity)又称为真阳性率(TPR), 为异常区域中被正确识别为阳性的比率, 是衡量一个系统真阳性识别性能的尺度.敏感的系统能够识别出要找的阳性个体, 同时很少产生假阴性.特异度(specificity), 又称为真阴性率(TNR), 是正常类别中被正确识别为阴性的比率.特异度衡量一个系统能够在多大程度上把阴性个体正确地识别出来或挑出那些不是期望的个体.假阳性率(FPR)是真阴性类别中被识别为阳性的比率.一个好的系统有很高的敏感度和特异度, 同时假阳性率极低.准确率(accuracy)是对象中真阳性和真阴性个体被正确识别的比率.精确度(precision)也称为阳性预测值(PPV), 是被识别为阳性个体中真阳性的比率.阴性预测值(NPV)是被识别为阴性的个体中的真阴性的比率.
受试者操作特征曲线是基于统计学决策理论产生的, 广泛应用于CAD系统评估中[
受试者操作特征曲线(ROC curve)
Receiver operating characteristic curve (ROC curve)
当一幅图像上有多个异常结果需要定性、定量、定位检测分析时, ROC方法无法完成评价任务.Bunch等人[
自由响应ROC曲线(FROC curve)
Free-Response receiver operating characteristic curve (FROC curve)
混淆矩阵也是评价CAD系统性能的一种常用方法[
混淆矩阵中, 元素的行下标对应目标的真实属性, 列下标对应分类器产生的识别属性.对角线元素表示各模式能够被分类器
本文引文中提到的CAD系统性能评价使用评价方法或指标分布如
本文引文所用性能评价指标分布图
Distribution chart of performance evaluation metrics used in the cited articles
基于医学图像的计算机辅助检测/诊断技术已经成为国内外研究的热点之一, 相关的理论和方法也得到不断发展和完善, 部分成果在实际医疗诊断中得到应用.优秀的算法和恰到好处的特征选择是卓越CAD系统的基础.
CAD系统所用算法
Algorithms used in CAD systems
文献 | 预处理 | 图像预分割 | ROI检测 | 降维及特征选择 | 假阳性去除/分类 |
[ |
- | 域值法 | 形态学 | - | 3D MTANN |
[ |
- | - | 峰值法 | - | - |
[ |
- | - | - | - | 贝叶斯公式 |
[ |
灰度正则化 | 最大后验概率-最大期望算法 | - | - | SVM |
[ |
- | - | - | “wrapper”方法 | ColonCAD原形 |
[ |
- | - | - | - | SVM |
[ |
- | - | 区域增长法、域值法 | - | |
[ |
- | - | 多阈值法 | - | 模板匹配、层次聚类法 |
[ |
拉普拉斯滤波 | - | - | PCA | Rotation森林 |
[ |
碟形模糊滤波 | - | 阈值法、大津法、Rosin法、正则分布法、高斯参数法、矩量保持法、Kapur和熵法、Kittler聚类、拓扑稳定状态法 | - | - |
[ |
- | 神经网络 | - | - | 神经网络 |
[ |
小波变换、阈值量化 | - | - | - | - |
[ |
- | - | - | - | |
[ |
降采样、局部对比增强 | 域值法 | 多域值法、形态学 | 顺序前进法(SFS) | 线性辨别(FLD)分类器、二次分类器 |
[ |
自适应维纳滤波(wiener) | 迭代阈值、形态学、多层前馈网络 | 区域增长法 | - | 多层神经网络 |
[ |
自适应中值滤波、CLAHE | - | 多阈值法 | - | 修改的BFGS |
[ |
Gabor、自动增强、FFT | - | 阈值法、Marker- Controlled Watershed | 二值化、Masking Approach | - |
[ |
区域增长、形态学 | 分水岭算法 | 阈值法 | PCA | SVM |
[ |
- | - | 单点集合分割算法 | 不相关特征递归消除 | |
[ |
- | - | 滚球、区域增长 | - | 3D SVM |
[ |
- | 拉普拉斯双边滤波 | - | - | |
[ |
- | 1-D大津法 | 2-D大津法 | 遗传算法 | 高斯混合模型 |
[ |
- | - | 3D Hessian、3D高斯平滑 | - | - |
[ |
- | 多域值 | 基于规则的方法 | - | SVM、Particle swarm algorithm |
[ |
- | - | - | LDA, GA | ANN |
[ |
- | - | - | GA | FFNN |
[ |
- | - | - | Fisher, GA | SVM、Bagging、朴素贝叶斯、 |
[ |
- | 高斯平滑、域值法 | 区域增长 | 多核特征选择 | SVM |
[ |
重采样 | - | - | - | 多尺度CNN、SVM |
[ |
- | 域值法、Channeler Ant Model | - | - | FFNN, Voxel-Based Neural Approach |
[ |
降采样、过采样 | - | - | - | SVM |
[ |
- | - | 区域增长法 | - | 随机森林、SVM、决策树、 |
[ |
降采样 | - | - | CNN, DBN, SDAE | |
[ |
单调三次分段插值 | - | - | SVM | |
[ |
中值滤波、直方图均衡化 | -- | - | - | ANN |
[ |
- | -- | - | - | 分类回归树 |
[ |
- | -- | FCM聚类分割 | - | 半监督FCM聚类 |
[ |
神经网络滤波器 | MTANNs | - | - | MTANNs |
[ |
- | - | - | OverFeat、SVM | |
[ |
- | 自适应域值法、动态规划算法 | 滑降算法 | CNN | |
[ |
中值滤波、有限对比适应性直方均衡化 | 自适应阈值法、活动边缘模型 | - | PCA | SVM |
[ |
CLAHE、2D自适应中值滤波 | 阈值法 | 阈值法 | SVM、朴素贝叶斯、 |
|
[ |
中值滤波、阈值、形态开、形态闭、膨胀、腐蚀 | 阈值法 | 阈值法、CLAHE、Sobel算子 | SVM | |
[ |
区域增长法、手动 | ||||
[ |
全局均衡变换、均值滤波 | 腐蚀、膨胀、Sobel操作、大津域值法 | 灰度量化 | PCA | 自动异常检测分类 |
[ |
缩放、二值化、腐蚀、区域确定 | ||||
[ |
模板匹配 | 阈值法 | |||
[ |
CNN、DBN、GMM、有条件随机场、结构化SVM | ||||
[ |
多尺度森增强、钙化信噪比增强 | 域值迭代、多域值法 | 域值迭代、对象增长 | 聚类 | |
[ |
阈值法 | 阈值法 | 小波分析 | 极限学习机(ELM) | |
[ |
自动域值和标签、顶帽滤波、伽马校正 | 伽柏滤波、域值法 | 域值法 | SVM | |
[ |
CLAHE | SVM | |||
[ |
Gabor filters | PCA | A2INET | ||
[ |
中值滤波 | 阈值法 | 空间模糊C-means | 顺序前向浮动选择 | 朴素贝叶斯 |
[ |
高斯差分、多层地形区域增长、活动边缘 | ANN | |||
[ |
GA | 神经网络 | |||
[ |
手动 | 动态域值法 | SVM | ||
[ |
多阈值法 | 集成融合、SVM、神经网络 | |||
[ |
正则化 | SVM | |||
[ |
线性霍夫变换 | 区域增长法 | SVM | ||
[ |
高斯滤波 | ANN | |||
[ |
模糊检测算法 | NN | BPNN | ||
[ |
- | - | - | - | 人工免疫半监督学习算法 |
[ |
局部对比正则化 | 大津法 | - | - | - |
[ |
Weiner滤波, 直方图均衡 | - | 区域增长法, 标记控制的分水岭分割 | - | - |
[ |
- | - | 主动轮廓模型 | 高斯分布曲线 | MLP |
[ |
手动 | - | - | Student t test or the Mann- Whitney U-test | 二值逻辑回归 |
[ |
- | - | - | independent- samples t-test | Wilks λ逐步统计 |
[ |
总变分模型 | 基于图的鲁棒方法 | - | 双聚类打分 | SVM |
[ |
- | 迭代分水岭法 | - | - | ANN |
[ |
- | - | 3D主动轮廓水平集 | - | - |
[ |
- | 全局主动轮廓、全局+局部主动轮廓 | - | 多线性回归 | LDA |
[ |
- | 区域增长法 | - | - | Student t-test |
[ |
阈值法、形态学开、形态学闭、空洞填充、连接成份抽取 | 边缘轮廓分析、曲面拟合、梯度向量流算法 | - | SVM | - |
[ |
- | - | - | 随机森林 | 随机森林+决策树 |
[ |
中值滤波 | 大津法 | 域值法、 |
- | SVM |
[ |
- | - | - | “R-包”随机森林 | SVM, RF, LDA |
[ |
- | 阈值法 | - | - | boosting tree |
[ |
形态学腐蚀 | 测地距离变换 | - | - | - |
[ |
- | 后验期望最大化、阈值法 | - | - | SVM |
[ |
- | - | - | - | SVM |
[ |
- | - | 手工 | PCA | Hotelling T-square |
[ |
隐马尔可夫模型 | - | - | - | 条件随机场 |
[ |
- | - | - | SFFS、AUC最大 | SVM |
[ |
- | - | - | 随机森林 | 位置指数嵌入式随机森林 |
[ |
- | 手动 | - | - | 邦弗朗尼-邓恩方法 |
[ |
配准、小波滤波 | - | - | - | 贝叶斯 |
[ |
- | - | - | - | LDA |
[ |
配准 | - | - | - | SVM |
[ |
配准 | 多图集分割算法 | - | - | - |
[ |
正则化 | 多水平集 | - | - | - |
[ |
- | 空间连续流最大化模型 | - | - | - |
[ |
- | 耦合水平集 | - | - | - |
[ |
中值滤波 | - | 阈值法 | - | 决策阈值法 |
[ |
- | - | - | t-test、交互信息、最小冗余最大关联 | LDA、SVM、 |
[ |
- | - | - | 组合SVM | SVM |
[ |
- | - | - | - | Inception v3、VGG-16 12 |
[ |
降采样、颜色正则化 | - | - | - | 多分辨增强率贝叶斯算法 |
[ |
- | - | 最大期望/后边缘最小化 | - | 统计关联规挖掘 |
[ |
西格玛滤波、梯度幅值滤波 | 水平集 | - | - | SPSS |
[ |
3D傅里叶变换、洛伦兹函数 | 模糊c-means聚类算法(FCM) | - | LDA | 二分类贝叶斯神经网络 |
[ |
正则化 | - | - | - | CBIR、 |
[ |
- | 自适应域值法、 |
- | - | |
[ |
- | - | - | - | LDA |
[ |
西格玛滤波、梯度幅值滤波 | 水平集、形态学闭合、3D细化算法、广度优先搜索和修剪算法 | - | - | - |
[ |
西格玛对比增强 | - | 最大期望分割法、 |
LDA | SVM |
[ |
- | 区域增长算法 | - | - | 二值逻辑回归 |
[ |
- | - | - | PCA | SVM |
[ |
- | - | - | 逐步法 | ANN |
[ |
- | 迭代选择性能级别评估 | 智能开算法 | - | Gentle Boost, RF |
[ |
线性插值、过采样 | 区域增长算法、模糊连接度计算 | - | 双列相关系数 | - |
[ |
序列西格玛滤波、梯度幅值滤波 | 水平集、形态学闭合 | - | - | SVM |
[ |
刚性和弹性配准 | - | 域值法 | - | - |
[ |
立方插值 | - | 交叉数算法、奇偶规则算法 | PCA | SVM |
[ |
弹性配准算法 | 直方图域值法 | - | - | SVM |
[ |
- | 主动轮廓模型 | - | - | SVM |
[ |
- | 大津法 | - | - | SVM |
[ |
- | - | 标准局部形状索引指数 | - | 贝叶斯多个实例关联向量机 |
[ |
- | - | - | 分层聚合和自组织映射 | MFF-NN, |
[ |
- | 基于增强阈值的分割 | - | - | SVM |
[ |
- | - | - | - | ANNs |
[ |
运动补偿 | - | - | SVM | |
[ |
密度正则化 | - | 正交方向螺旋扫描技术 | - | SVM |
[ |
图像配准 | - | - | PCA | SVM |
[ |
正规化 | - | - | - | 词汇树、 |
[ |
重采样、密度正则化 | - | 螺旋扫描算法 | - | SVM |
[ |
- | - | - | Fuzzy-Omega算法 | 模糊逻辑 |
[ |
高斯低通滤波、空白遮罩滑动(去光晕)、直方图拉伸(对比增强) | 自适应域值、 |
- | 顺序向前选择法 | kNN |
[ |
- | 自动域值法 | - | - | - |
[ |
- | - | 阈值法 | - | SVM |
[ |
拉普拉斯算子、平滑滤波 | Kirsch算子、二值分割、小波变换 | - | PCA | SVM |
[ |
- | - | - | - | 朴素贝叶斯、逻辑回归 |
[ |
Horn和Schunck方法(运动补偿) | 交互区域增长法 | - | - | SVM |
[ |
CLAHE | - | - | - | ESN(回声状态网) |
[ |
- | - | 域值法 | - | SVM、AdaBoost |
[ |
立方插值法 | - | 高斯差分 | - | 核函数主成份分析 |
[ |
- | 主动轮廓模型 | - | LDA | |
[ |
CLAHE、各向异性扩散滤波、Gabor滤波 | 分水岭 | - | - | Fisher discriminant analysis (FLDA) |
[ |
- | - | - | - | 分类回归决策树 |
[ |
直方图均衡化 | - | - | SVM特性评价 | SVM, |
[ |
直方图均衡化 | - | - | SVM | |
[ |
- | 自动域值法、 |
分水岭法 | - | |
[ |
贝叶斯分类 | 均值漂移分割 | - | - | ANN, SVM |
[ |
同态滤波 | 阈值法 | - | - | |
[ |
图像配准、Horn和Schunck方法运动补偿 | - | - | - | SVM |
[ |
累积和方法(直方图拉伸)、CLAHE | - | 墨西哥帽子滤波、霍夫变换、大津法、模糊C-Means聚合、带标记控制的分水岭变换 | - | SVM, MLP, LVQ, PNN |
[ |
- | - | - | - | SVM |
[ |
直方图均衡化、小波变换 | - | - | - | 模糊多参支持向量机 |
[ |
- | - | - | PCA | PCA、ICA、模糊分类器 |
[ |
- | 二值化 | - | - | 模糊系统 |
[ |
峰值信噪比、中值滤波 | - | 区域增长法 | - | - |
[ |
- | - | 小波变换 | 基于相关性特征选择器 | 三步骤半监督学习 |
[ |
- | - | 随机森林半监督学习 | - | - |
[ |
- | - | - | - | 自适应数据编辑联合训练类型随机森林 |
[ |
多域值算法 | - | - | 多算法融合 | |
[ |
密度正则化 | - | CNN | - | - |
[ |
B样条插值 | - | - | - | GoogLeNet, VGG, Overfeat, AlexNet, CifarNet |
[ |
过采样、正则化 | - | - | - | CNN、SVM |
[ |
降采样、3D拉普拉斯算子、正则化 | - | 3D聚类、活动边缘 | - | 深度CNN、LDA |
[ |
- | - | - | - | AlexNet |
[ |
傅里叶变换、滤波反投影法 | - | - | FDR | SVM |
[ |
- | 区域增长法 | - | 最佳优先搜索算法 | LDA |
[ |
- | 区域增长算法 | 交替C均值聚类 | - | 概率神经网络 |
[ |
平面场正则化、中值滤波 | - | - | - | SVM |
[ |
- | - | 混合域值法 | 增益比例特征选择 | SVM、RF、逻辑模型树、隐朴素贝叶斯 |
去假阳性或分类时所用特征
Features used in classification or false positive reducement
特征类 | 特征子类 | 文献 |
几何特征 | - | [ |
- | 直径 | [ |
- | 体积特征 | [ |
- | 长短轴比率 | [ |
- | Zernike矩 | [ |
形状特征 | - | [ |
- | 圆度特征 | [ |
- | 毛刺特征 | [ |
- | 3D形态学特征 | [ |
纹理特征 | - | [ |
- | 3D纹理特征 | [ |
- | 梯度特征 | [ |
- | GLCM相关特征 | [ |
- | Haralick特征 | [ |
- | 3D Haralick特征 | [ |
- | Gabor特征 | [ |
- | LBP特征 | [ |
- | SIFT | [ |
- | DT-CWT | [ |
- | 小波特征 | [ |
- | 灰度直方图 | [ |
- | 二阶统计特征 | [ |
密度特征 | - | [ |
- | 3D CT值 | [ |
颜色特征 | - | [ |
卷积特征(CNN) | - | [ |
高层语义特征 | - | [ |
动力学特征 | - | [ |
人口学特征 | - | [ |
声学特征 | - | [ |
弹性图像特征 | - | [ |
近些年, 越来越多的CAD系统被提出来, 但由于医学图像本身内容结构复杂、医学征象标准库建立困难等原因, 在医学图像CAD系统研究方面依然面临着挑战.
(1) 有效标注的样本量太小.
在对文献中提到的CAD系统所用算法作统计的同时, 我们也对各CAD系统训练所用的训练图像样本进行统计, 发现样本图像数量超过5 000的仅3个, 仅使用几十个或100个左右样本量的不算少, 而根据经验法则, 训练时用的数据越多, 系统性能就越好[
(2) 系统的性能不好评估.
公用的基准库是公平、正确地对比衡量CAD系统性能的基础条件之一.目前, 医学类的公共征象库有美国肺部图像数据库联盟的LIDC/IDRI库、日本放射技术学会的JSRT库、弗雷德里克国家实验室的RIDER、ELCAP公用库、南佛罗里达州大学的DDSM、Mini-MIAS乳腺征象库、NCIA、TCIA Collection库、promise12前列腺库, 国内的有LISS[
(3) CAD系统应用于临床使用有很多困难.
首先, 由于身体器官的医学图像构成复杂, 如肺部器官多、内部结构显示多态化, 医学图像上各种组织灰度相近.乳腺的图像相对简单, 图像上没有其他器官、症状的干扰, 目前有少量临床的商用系统.一个实验室的CAD研究实验往往针对某一类征象, 而临床应用要检测所有征象, 甚至是几种疾病同时伴发的征像组合.由于医院的商用医学影像系统的接口不对外开放, 开发的CAD系统很难与医院医生所用的系统无缝结合.医院的日常医疗工作任务重, 医生没有单独的时间对系统试用评估.目前在医学图像领域开展的研究较多, 但临床应用难度较大.
(4) CAD系统的应用效果还不理想.
尽管有的系统实验结果性能非常好, 但这只是小样本量、特定案例下的测试, 一旦实验对象变成普通随机病例, 如临床的医学图像, 情况会复杂得多[
目前, 商用的系统有IQQA-Chest、CyclopusCAD® mammo、SecondLook和ImageChecker等.尽管已取得了一些成果, 但CAD系统的临床检测和诊断的正确率还偏低, 不少临床研究显示, 现有的CAD方法或系统的应用效果并不明显[
基于医学图像的CAD系统研究还处于未成熟阶段, 有很多工作需要我们去探索和认识.对未来医学图像CAD系统的可能发展趋势, 我们展望如下.
(1) 既检测又诊断的CAD系统能够为医生提供更丰富的信息.
CADe系统只对病变进行检测, 而不做出诊断.对于医生来说, 临床应用程序不提供病变的影像学特征, 体现的信息是不全面的.CADx系统如果只显示病变的良恶性诊断结果而不显示检测到的病变信息也不完善.未来的CAD系统应该是CADe系统和CADx系统的功能结合.
(2) 与医院HIS系统或PACS系统相结合的临床应用.
目前的研究中基于实验室样本库的较多, 仅有少数商用系统[
(3) 半监督学习.
传统的分类器只使用标记数据进行训练, 但充足的标记样本通常难以获得(如医学图像)、昂贵或耗时, 因为它们需要经验丰富的注释人员的努力.同时, 未标记的数据可能相对容易收集, 但几乎没有办法加以使用[
(4) 每种算法都有其局限性, 目前的单一分类器都不能完全解决所有的问题或者达到应用系统的要求.
多算法融合, 综合运用各种方法的优势、扬长避短, 组合起来可以得到更高鲁棒性的系统[
(5) 在CAD系统中引入专家系统的思想, 使用时医生可以与系统交互.
在分割、检测阶段, 医生对环节的输出结果进行更准确的修改.在分类阶段对分类正确的结果给予奖励, 对分类错误的结果给予惩罚.系统根据回馈结果再进行强化学习, 逐步达到完美的性能.
(6) 深度学习.
基于视觉语义的医学图像分类一直以来都是充满挑战的研究领域, 不仅待识别的图像种类繁多, 在每一类图像的内部也存在诸多变数, 包括光照变化、不匹配不对齐、形变、遮挡等因素.针对这些变数, 学者们做了各种各样的努力, 提出了各种各样的特征来应对这些变化, 如比较典型的SIFT和HOG特征.虽然这些特征能够很好地应对特定情况下的数据处理任务, 但这些特征的泛化能力有限[
基于医学图像的CAD技术研究具有重要的医疗和社会价值.本文以不同的身体部位为主线, 按照医学图像产生的技术分类, 调研了2012年以来研究较多的各类医学图像CAD系统, 并进行多维度归纳梳理, 还有一些其他身体部位的医学CAD技术研究, 如脑部[
在很长一段时间内, 计算机辅助检测/诊断将是医学图像处理领域的研究热点.建立鲁棒和高性能的CAD系统, 能够更好地辅助医生对疾病的检测与诊断, 提高患者的生存率, 改善其生活质量, 具有广阔的应用前景.
英文缩写与全称对照表
缩写 | 全称 |
A2INET | Adaptive artificial immune network |
ACS | American cancer society |
AFE | Autofluorescence endoscopy |
ANN | Artificial neural network |
AUC | Area under curve |
bCT | Breast computed tomography |
BFGS | Broyden-fletcher-goldfarb-shanno |
BPNN | Back propagation neural network |
CADe | Computer-Aided detection |
CADx | Computer-Aided diagnosis |
CBIR | Content-Based image retrieval |
CC | Craniocaudal |
CFS | Correlation-Based feature subset selection for machine learning |
CLAHE | Contrast limited adaptive histogram equalization |
CNN | Convolutional neural network |
CRC | Colorectal cancer |
CT | Computerized tomography |
CTC | Ct colonography |
CV | Chan-Vese |
CXR | Chest X radiograph |
DBN | Deep belief network |
DDSM | The digital database for screening mammography |
ELCAP | The end-use load and consumer assessment program |
ELM | Extreme learning machine |
ESN | Echo state network |
FCM | Fuzzy c-means |
FDR | Fisher linear discriminant ratio |
FFNN | Feed-Forward neural network |
FFT | Fast fourier transform |
FLD | Fisher linear decriminant |
FLDA | Fisher discriminant analysis |
FP | False positive |
FPR | False positive rate |
FROC | Free-Response roc curve |
GA | Genetic algorithm |
GGO | Ground-glass opacity |
GMM | Gaussian mixture model |
GLCM | Gray-Level co-occurrence matrix |
GLRLM | Gray level run length matrix |
IDRI | The infectious disease research institute |
JSRT | Japanese society of radiological technology |
kNN | |
LDA | Linear discriminant analysis |
LI | Location index |
LIDC | Lung image database consortium |
LISS | Lung ct imaging signs |
LVQ | Learning vector quantization |
MFFNN | Multi-Layer feed forward neural network |
MIAS | The mammographic image analysis society |
MLO | Medio lateral-oblique |
MLP | Multilayer preceptor |
MR | Magnetic resonance |
MRI | Magnetic resonance imaging |
MTANN | Massive-Training artificial neuralnetwork |
MTB | Mycobacterium tuberculosis |
NC | Nearest centroid |
NCIA | National cancer image archive |
NN | Neural network |
OC | Optical colonoscopy |
PACS | Picture archiving and communication systems |
PCA | Principal component analysis |
PNN | Probabilistic neural network |
PPV | Positive predictive value |
RF | Random forest |
RFNC | Relief-f non-correlated |
RIF | Resistance to Rifampicin |
ROC | Receiver operating characteristic |
ROI | Regions of interest |
RRF | Rocchio relevance feedback algorithm |
SCLGM | Square centroid lines gray level distribution method |
SDAE | Stacked denoising autoencoder |
SFS | Sequential forward selection |
SVM | Support vector machine |
TCIA | The cancer imaging archive |
TNR | True negative tate |
TPR | True positive rate |
VOI | Volume of intretest |
wkNN | Weighted knn |
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