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摘要: 深度学习（Deep learning，DL），特别是深度卷积神经网络（Convolutional neural networks，CNNs），能够从医学图像大数据中自动学习提取隐含的疾病诊断特征，近几年已迅速成为医学图像分析研究热点.本文首先简述医学图像分析特点；其次，论述深度学习基本原理，总结深度CNNs在医学图像分析中的分类、分割框架；然后，分别论述深度学习在医学图像分类、检测、分割等各应用领域的国内外研究现状；最后，探讨归纳医学图像分析深度学习方法挑战及其主要应对策略和开放的研究方向.Abstract: Deep learning (DL) algorithms, such as convolutional neural networks (CNNs), can automatically extract hidden disease diagnosis features from medical image data, and are being used to analyze medical images now. We review most of the deep learning methods for medical image analysis. Firstly, we introduce the characteristics of medical image analysis briefly. Then, we analyze the principles of deep learning, highlight the popular CNNs and summarize the frameworks of image classification and segmentation. Thirdly, we describe the state-of-the-art of the medical image analysis methods based on deep learning. Finally, we discuss the challenges and practicable strategies in deep learning for medical image analysis, as well as open research.1) 本文责任编委 桑农
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图 2 受限玻尔兹曼机RBM及基于RBM的深度网络
Fig. 2 Restricted Boltzmann machine (RBM) and deep networks based RBM
表 1 基于CNN的计算机视觉分类任务经典框架
Table 1 Classical CNN frameworks for computer vision classification tasks
网络结构 特点 备注 LeNet[9] 多个卷积层和子采样层 美国手写数字识别 AlexNet[60] 提出了ReLU和Dropout 刷新了2012年ImageNet ILSVRC物体分类竞赛的世界纪录 VGGNet[62] 提出采用小卷积核实现更深的网络以及多尺度融合 获ILSVRC 2014定位任务冠军、分类任务亚军 GoogleNet[65] 22层网络, 多个Inception结构串联 获ILSVRC 2014分类和检测任务冠军 ResNet[14] 提出了残差网络, 引入跳转连接, 深达152层 2015年ILSVRC物体检测与物体识别竞赛冠军 Inception ResNet[67] Inception结构与Residual Net结合 可获得与ResNet相当的性能, 但收敛速度加快 FCN[68] 密集性预测, 实现了像素级分类 避免了图像块之间的重叠而导致重复卷积计算的问题 DenseNet[70] 任何两层之间都有直接的连接 缓解梯度消失, 强化特征传播, 支持特征重用, 并降低网络参数数量 SqueezeNet[72] 简化网络结构和减少网络参数 仅需1/50的AlexNet参数量即可达到了AlexNet相同的精度 DCNN[73] 提出可变形深度卷积神经网络 增强了网络对于几何变换的建模能力 DPN[71] 结合了ResNet和DenseNet优势 基于DPN的团队取得2017年ILSVRC物体检测与物体识别竞赛冠军 SENet[74] 学习每个特征通道的重要程度, 强化有用特征 2017年ILSVRC图像分类任务竞赛冠军 
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表 2 脑瘤分割方法比较(使用BRATS数据集验证)
Table 2 Comparison of methods for brain tumor segmentation (validation on BRATS database)
作者 方法 DICE 总肿瘤区 核心肿瘤区 活性肿瘤区 专家评定 医学训练和经验 0.88 0.93 0.74 Urban[174] 多模态输入, 训练3D CNN 0.87 0.77 0.73 Zikic[175] 将3D立方体图像块转换成2D图像块, 训练2D CNN网络 0.837 0.736 0.69 Havaei[82] 2D多模态输入, 双路径级联CNN架构, 综合了局部细节和更全局信息 0.88 0.79 0.73 Pereira[176] 3×3的小的小卷积核, 更多的CNN层数和非线性运算, 更少的滤波器权重 0.88 0.83 0.77 Kamnitsas[168] 采用深度为11层的小滤波器3D CNN的双路径网络框架 0.898 0.75 0.721
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