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级联稀疏卷积与决策树集成的病理图像细胞核分割方法

宋杰 肖亮 练智超

宋杰, 肖亮, 练智超. 级联稀疏卷积与决策树集成的病理图像细胞核分割方法. 自动化学报, 2021, 47(2): 378−390 doi: 10.16383/j.aas.c190672
引用本文: 宋杰, 肖亮, 练智超. 级联稀疏卷积与决策树集成的病理图像细胞核分割方法. 自动化学报, 2021, 47(2): 378−390 doi: 10.16383/j.aas.c190672
Song Jie, Xiao Liang, Lian Zhi-Chao. Cascade sparse convolution and decision tree ensemble model for nuclear segmentation in pathology images. Acta Automatica Sinica, 2021, 47(2): 378−390 doi: 10.16383/j.aas.c190672
Citation: Song Jie, Xiao Liang, Lian Zhi-Chao. Cascade sparse convolution and decision tree ensemble model for nuclear segmentation in pathology images. Acta Automatica Sinica, 2021, 47(2): 378−390 doi: 10.16383/j.aas.c190672

级联稀疏卷积与决策树集成的病理图像细胞核分割方法

doi: 10.16383/j.aas.c190672
基金项目: 国家自然科学基金(61871226, 62001247), 国家重点研发计划(2016YFF0103604), 中央高校基本科研专项资金(30918011104), 江苏省社会发展重点研发计划(BE2018727), 南京邮电大学引进人才科研启动基金(NY219152)资助, 江苏省高等学校自然科学研究面上项目(20KJB520005)资助
详细信息
    作者简介:

    宋杰:博士, 南京邮电大学自动化学院、人工智能学院讲师. 主要研究方向为生物医学图像处理, 机器学习与模式识别.E-mail: j.song0041@gmail.com

    肖亮:博士, 南京理工大学计算机科学与工程学院教授. 主要研究方向为信号处理, 计算机视觉, 生物医学图像处理, 机器学习与模式识别. 本文通信作者. E-mail: xiaoliang@mail.njust.edu.cn

    练智超:博士, 南京理工大学计算机科学与工程学院副教授. 主要研究方向为医学图像处理, 神经影像, 机器学习与模式识别.E-mail: newlzcts@gmail.com

Cascade Sparse Convolution and Decision Tree Ensemble Model for Nuclear Segmentation in Pathology Images

Funds: Supported by National Natural Science Foundation of China (61871226, 62001247), National Major Research Plan of China (2016YFF0103604), Fundamental Research Funds for the Central Universities (30918011104), Jiangsu Provincial Social Developing Project (BE2018727), NUPTSF (NY219152), Natural Science Foundation for Colleges and Universities in Jiangsu Province (20KJB520005)
  • 摘要: 数字病理图像分析对于乳腺癌、肾癌等良恶性分级诊断具有重要意义, 其中细胞核的形态测量是病理量化分析的关键. 然而, 由于病理图像背景复杂, 细胞核高密度分布、细胞粘连等, 个体细胞核精准分割是一个挑战性问题. 本文提出一个级联稀疏卷积与决策树集成学习的细胞核分割模型. 该模型由稀疏可分离卷积模块和集成决策树学习的正则化回归模块堆叠级联组成, 其中: 前者采取秩-1张量分解学习机制, 可分层抽取细胞核的多尺度方向分布式抽象特征; 而后者采取随机采样、树剪枝以及正则化回归机制提升逐像素回归分类能力. 相比于现有深度学习模型, 该模型无需非线性激活和后向传播计算, 参数规模较小, 可实现端到端的学习. 通过乳腺、前列腺、肾脏、胃和膀胱等多组病理图像的分割实验表明: 该模型能够实现复杂数字病理图像中的高密度细胞核的快速个体目标检测和分割, 在Jaccard相似性系数、F1分数和平均边缘距离三个指标上均优于目前CNN2、CNN3和U-Net等深度学习方法, 具有较好应用前景.
  • 图  1  病理细胞核图像分割的挑战与人工分割结果

    Fig.  1  Challenges in nuclear segmentation and associated ground truth

    图  2  用于病理图像分割的CNN体系结构

    Fig.  2  The CNN-style architecture for pathology image segmentation

    图  3  用于病理图像分割的U-Net体系结构

    Fig.  3  The U-Net-style architecture for pathology image segmentation

    图  4  两层CscDTE学习模型示例

    Fig.  4  Example of two-layer CscDTE architecture

    图  5  基于张量分解技术学习一组秩-1可分离核

    Fig.  5  Tensor decomposition for learning rank-1 separable kernels

    图  6  CscDTE框架中的集成决策树学习模块的训练过程

    Fig.  6  Flowchart of the training procedure for the decision tree ensemble learning module of CscDTE framework

    图  7  基于本文提出的CscDTE模型的分割改进

    Fig.  7  Improvement obtained by our CscDTE model

    图  8  Kumar病理细胞核相同器官图像的分割比较

    Fig.  8  Comparative segmentation results on the Kumar same-organs testing pathology images

    图  9  Kumar病理细胞核不同器官图像的分割比较

    Fig.  9  Comparative segmentation results on the Kumar different-organs testing pathology images

    图  10  不同算法在具有重叠、形状及大小不规则的病理图像上的轮廓可视化分割结果图

    Fig.  10  Visualizing segmented contours of different algorithms on the pathology image with shape and color variations and overlapping regions

    图  11  本文方法与U-Net方法在KIRC数据集上的分割比较

    Fig.  11  Comparative segmentation using ours and U-Net on the KIRC dataset

    表  1  提出的CscDTE模型的最优参数值. 像素位置样本总数为800 000

    Table  1  The optimal hyper-parameter values of our CscDTE model. The total number of pixel samples is 800 000

    数据集 $N$ $L$ ${d^2(R)}$( ${1^{\rm{st}}}$layer; ${2^{\rm{nd}}\sim L^{\rm{th}}}$layers) $M$
    TCGA WSIs 200 000 5 $11^2$(25), $17^2$(8), $21^2$(72), $29^2$(8), $43^2$(44); $11^2$(25), $21^2$(36), $43^2$(36) 50
    下载: 导出CSV

    表  2  用于病理图像分割的CNN2体系结构

    Table  2  The CNN2 architecture for pathology image segmentation

    网络层 滤波器尺寸 激活 输出尺寸 随机丢弃率
    输入 $ {\rm{55}} \times {\rm{55}} \times {\rm{3}} $
    卷积 1 $ {\rm{6}} \times {\rm{6}} $ ReLU $ {\rm{50}} \times {\rm{50}} \times {\rm{48}} $ 0.1
    池化 1 $ {\rm{2}} \times {\rm{2}} $ Max $ {\rm{25}} \times {\rm{25}} \times {\rm{48}} $
    卷积 2 $ {\rm{4}} \times {\rm{4}} $ ReLU $ {\rm{22}} \times {\rm{22}} \times {\rm{48}} $ 0.2
    池化 2 $ {\rm{2}} \times {\rm{2}} $ Max $ {\rm{11}} \times {\rm{11}} \times {\rm{48}} $
    全连接 1 ReLU $ {\rm{1\,024}} \times {\rm{1}} $ 0.5
    全连接 2 ReLU ${\rm{1\,024} } \times {\rm{1} }$ 0.5
    输出 SoftMax $ 2 \times 1 $
    下载: 导出CSV

    表  3  用于病理图像分割的CNN3体系结构

    Table  3  The CNN3 architecture for pathology image segmentation

    网络层 滤波器尺寸 激活 输出尺寸 随机丢弃率
    输入 $ {\rm{51}} \times {\rm{51}} \times {\rm{3}} $
    卷积 1 $ {\rm{4}} \times {\rm{4}} $ ReLU $ {\rm{48}} \times {\rm{48}} \times {\rm{25}} $ 0.1
    池化 1 $ {\rm{2}} \times {\rm{2}} $ Max $ {\rm{24}} \times {\rm{24}} \times {\rm{25}} $
    卷积 2 $ {\rm{5}} \times {\rm{5}} $ ReLU $ {\rm{20}} \times {\rm{20}} \times {\rm{50}} $ 0.2
    池化 2 $ {\rm{2}} \times {\rm{2}} $ Max $ {\rm{10}} \times {\rm{10}} \times {\rm{50}} $
    卷积 3 $ {\rm{6}} \times {\rm{6}} $ ReLU $ {\rm{5}} \times {\rm{5}} \times {\rm{80}} $ 0.25
    池化 3 $ {\rm{2}} \times {\rm{2}} $ Max $ {\rm{3}} \times {\rm{3}} \times {\rm{80}} $
    全连接 1 ReLU ${\rm{1\,024} } \times {\rm{1} }$ 0.5
    全连接 2 ReLU ${\rm{1\,024} } \times {\rm{1} }$ 0.5
    输出 SoftMax $ 3 \times 1 $
    下载: 导出CSV

    表  4  本文方法与其他方法在Kumar病理数据集上的分割性能(均值)比较

    Table  4  Performance comparison of other methods with our CscDTE (For each metric, the mean are listed)

    器官 图像 Jaccard相似性系数 精确率 F1分数 平均边缘距离
    本文 U-Net CNN3 CNN2 分水岭 本文 U-Net CNN3 CNN2 分水岭 本文 U-Net CNN3 CNN2 分水岭 本文 U-Net CNN3 CNN2 分水岭
    乳腺 1 0.594 0.539 0.570 0.569 0.545 0.810 0.832 0.756 0.764 0.591 0.745 0.700 0.726 0.725 0.706 14.90 42.78 29.68 64.46 42.36
    2 0.675 0.613 0.603 0.448 0.592 0.832 0.803 0.844 0.853 0.625 0.806 0.760 0.752 0.619 0.744 12.99 13.12 14.34 62.27 18.49
    前列腺 1 0.646 0.634 0.656 0.625 0.585 0.867 0.858 0.858 0.871 0.679 0.785 0.776 0.792 0.769 0.739 12.56 15.87 11.41 10.22 24.40
    2 0.342 0.485 0.459 0.387 0.409 0.834 0.827 0.906 0.832 0.603 0.510 0.653 0.629 0.558 0.581 88.69 34.51 40.34 56.01 37.81
    肾脏 1 0.692 0.673 0.641 0.691 0.570 0.752 0.782 0.848 0.755 0.606 0.818 0.804 0.781 0.817 0.726 12.14 15.25 19.47 12.24 30.03
    2 0.513 0.539 0.524 0.463 0.512 0.765 0.789 0.763 0.681 0.570 0.678 0.701 0.687 0.633 0.677 16.35 21.48 12.73 21.38 9.48
    膀胱 1 0.603 0.551 0.581 0.550 0.325 0.846 0.756 0.720 0.795 0.330 0.752 0.711 0.735 0.709 0.490 41.97 86.13 22.91 52.33 79.61
    2 0.759 0.629 0.580 0.656 0.508 0.867 0.898 0.926 0.909 0.516 0.863 0.772 0.734 0.792 0.674 6.58 14.32 17.09 19.03 22.04
    1 0.781 0.645 0.622 0.577 0.647 0.881 0.920 0.824 0.913 0.692 0.877 0.784 0.767 0.732 0.785 1.99 7.44 8.49 9.16 3.67
    2 0.785 0.641 0.714 0.623 0.679 0.891 0.931 0.948 0.910 0.736 0.880 0.781 0.833 0.767 0.809 1.09 6.82 5.41 6.75 1.76
    相同器官 0.577 0.581 0.576 0.531 0.536 0.810 0.815 0.829 0.793 0.612 0.724 0.732 0.728 0.687 0.696 26.27 23.84 21.33 37.76 27.10
    不同器官 0.732 0.617 0.624 0.602 0.540 0.871 0.876 0.855 0.882 0.569 0.843 0.762 0.767 0.750 0.690 12.91 28.68 13.48 21.82 26.77
    整体 0.639 0.595 0.595 0.559 0.537 0.835 0.840 0.839 0.828 0.595 0.771 0.744 0.744 0.712 0.693 20.93 25.77 18.19 31.39 26.97
    下载: 导出CSV

    表  5  本文方法与其他方法在Kumar病理数据集上的分割性能(标准差)比较

    Table  5  Performance comparison of other methods with our CscDTE (For each metric, the standard deviation are listed)

    器官 Jaccard相似性系数 精确率 F1分数 平均边缘距离
    本文 U-Net CNN3 CNN2 分水岭 本文 U-Net CNN3 CNN2 分水岭 本文 U-Net CNN3 CNN2 分水岭 本文 U-Net CNN3 CNN2 分水岭
    乳腺 0.057 0.052 0.023 0.086 0.033 0.016 0.021 0.062 0.063 0.024 0.043 0.042 0.018 0.075 0.027 1.35 20.97 10.85 1.55 16.88
    前列腺 0.215 0.105 0.139 0.168 0.125 0.023 0.022 0.034 0.028 0.054 0.195 0.087 0.115 0.149 0.112 53.83 13.18 20.46 32.38 9.48
    肾脏 0.127 0.095 0.083 0.161 0.041 0.009 0.005 0.060 0.052 0.026 0.099 0.073 0.067 0.130 0.035 2.98 4.41 4.77 6.46 14.53
    膀胱 0.110 0.055 0.001 0.075 0.129 0.015 0.100 0.146 0.081 0.132 0.079 0.043 0.001 0.059 0.130 25.02 50.78 4.12 23.55 40.71
    0.003 0.003 0.065 0.033 0.023 0.007 0.008 0.088 0.002 0.031 0.002 0.002 0.047 0.025 0.017 0.64 0.44 2.18 1.70 1.35
    相同器官 0.132 0.071 0.075 0.117 0.069 0.044 0.029 0.058 0.072 0.037 0.116 0.057 0.062 0.099 0.061 30.62 12.09 11.45 25.79 12.23
    不同器官 0.087 0.044 0.063 0.047 0.161 0.020 0.081 0.105 0.058 0.185 0.061 0.034 0.046 0.037 0.145 19.52 38.45 8.00 21.02 36.39
    整体 0.136 0.062 0.071 0.098 0.106 0.047 0.061 0.076 0.078 0.113 0.112 0.049 0.057 0.084 0.096 26.37 24.09 10.52 24.18 22.91
    下载: 导出CSV

    表  6  本文方法与U-Net在KIRC数据集上的分割性能(均值, 标准差)比较

    Table  6  Performance comparison of U-Net with our CscDTE (For each metric, the mean and standard deviation are listed)

    KIRC Jaccard系数 精确率 F1分数 平均边缘距离
    均值 标准差 均值 标准差 均值 标准差 均值 标准差
    本文方法 0.735 0.054 0.832 0.050 0.846 0.037 22.66 9.51
    U-Net 0.642 0.064 0.742 0.093 0.780 0.049 57.78 23.11
    下载: 导出CSV

    表  7  不同算法在病理数据集上的参数规模

    Table  7  Number of parameters of different algorithms on the pathology dataset

    方法 本文算法 U-Net CNN3 CNN2
    参数规模 184KB 1.75MB 1.97MB 7.04MB
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-09-23
  • 录用日期:  2020-03-12
  • 网络出版日期:  2021-02-26
  • 刊出日期:  2021-02-26

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