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基于多模型融合的肺部CT新冠肺炎病灶区域自动分割

史天意 程枫 李震 郑传胜 许永超 白翔

史天意, 程枫, 李震, 郑传胜, 许永超, 白翔. 基于多模型融合的肺部CT新冠肺炎病灶区域自动分割. 自动化学报, 2021, 47(x): 1−13 doi: 10.16383/j.aas.c210400
引用本文: 史天意, 程枫, 李震, 郑传胜, 许永超, 白翔. 基于多模型融合的肺部CT新冠肺炎病灶区域自动分割. 自动化学报, 2021, 47(x): 1−13 doi: 10.16383/j.aas.c210400
Shi Tian-Yi, Cheng Feng, Li Zhen, Zheng Chuan-Sheng, Xu Yong-Chao, Bai Xiang. Automatic segmentation of covid-19 infected regions in chest CT images based on 2D/3D model ensembling. Acta Automatica Sinica, 2021, 47(x): 1−13 doi: 10.16383/j.aas.c210400
Citation: Shi Tian-Yi, Cheng Feng, Li Zhen, Zheng Chuan-Sheng, Xu Yong-Chao, Bai Xiang. Automatic segmentation of covid-19 infected regions in chest CT images based on 2D/3D model ensembling. Acta Automatica Sinica, 2021, 47(x): 1−13 doi: 10.16383/j.aas.c210400

基于多模型融合的肺部CT新冠肺炎病灶区域自动分割

doi: 10.16383/j.aas.c210400
基金项目: 新一代人工智能重大项目(2018AAA0100400), 华中科技大学新型冠状病毒应急科技攻关专项(No.2020kfyXGYJ093, No.2020kfyXGYJ094, No.2020kfyXGYJ021), 国家自然科学基金(61703171)资助
详细信息
    作者简介:

    史天意:华中科技大学电子信息与通信工程学院博士研究生. 主要研究方向为图像分割, 医学图像分析与深度学习. E-mail: shitianyihust@hust.edu.cn

    程枫:华中科技大学电子信息与通信工程学院硕士. 主要研究方向为图像分割, 医学图像分析与深度学习. E-mail: chengfeng@hust.edu.cn

    李震:医学博士, 主任医师, 华中科技大学附属同济医院放射科教授. 主要研究方向为腹部影像诊断, 磁共振功能成像及医学图像分析. E-mail: zhenli@hust.edu.cn

    郑传胜:教授, 主任医师, 华中科技大学同济医学院医学影像系主任, 武汉协和医院放射科主任/介入科主任. 主要临床与研究方向为放射诊断和介入治疗, 重大疾病的影像学基础与临床. E-mail: hqzcsxh@sina.com

    许永超:华中科技大学电子信息与通信工程学院副教授. 主要研究方向为数学形态学, 图像分割, 医学图像分析和深度学习. 本文通讯作者. E-mail: yongchaoxu@hust.edu.cn

    白翔:华中科技大学人工智能与自动化学院教授, 国家防伪工程中心副主任. 主要研究方向为物体识别, 形状分析, 自然场景文字识别和智能系统. E-mail: xbai@hust.edu.cn

Automatic Segmentation of Covid-19 Infected Regions in Chest CT Images based on 2D/3D Model Ensembling

Funds: Supported by the National Key Research and Development Program of China under Grant No. 2018AAA0100400, HUST COVID-19 Rapid Response Call (No. 2020kfyXGYJ093, No. 2020kfyXGYJ094, No.2020kfyXGYJ021), National Natural Science Foundation of China (No. 61703171)
More Information
    Author Bio:

    SHI Tian-Yi Ph.D. candidate at the School of Electronic Information and Communications, Huazhong University of Science and Technology. His main research interest is image segmentation, medical image analysis, and deep learning

    CHENG Feng Master student at the School of Electronic Information and Communications, Huazhong University of Science and Technology. His main research interest is image segmentation, medical image analysis, and deep learning

    LI Zhen Ph.D., chief physician, professor at the Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology. His main research interest covers abdominal imaging diagnosis, functional magnetic resonance imaging, and medical image analysis

    ZHENG Chuan-Sheng Professor, chief physician, the director of the Department of Medical Imaging, Tongji Medical College, Huazhong University of Science and Technology, and the director of the Department of Radiology and Interventional Radiology, Wuhan Union Hospital. His research interest covers radiological diagnosis, interventional therapy, imaging basics, and clinical aspects of major diseases

    XU Yong-Chao Associate professor at the School of Electronic Information and Communications, Huazhong University of Science and Technology. His research interest covers mathematical morphology, image segmentation, medical image analysis, and deep learning. Corresponding author of this paper

    BAI Xiang Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology (HUST), and the Vice-director of the National Center of Anti-Counterfeiting Technology, HUST. His research interest covers object recognition, shape analysis, scene text recognition, and intelligent systems

  • 摘要: 自2019年末以来, 全球蔓延的新型冠状病毒(Coronavirus disease 2019, COVID-19)已经给世界人民造成了严重的健康威胁. 其中COVID-19患者的计算机断层扫描(Computed tomography, CT)图像通过肺炎病灶分割技术可以为医学诊断提供有价值的量化信息. 虽然目前基于深度学习的方法已经在COVID-19肺炎病灶分割任务上取得了良好的效果, 但是在面对不同中心数据的情况下分割效果往往会大幅下降. 因此, 研究一种具有更好泛化性能的COVID-19肺炎病灶分割算法具有重要意义. 本文中, 我们提出了一种新冠肺炎病灶多模型融合分割方法. 具体来说, 我们通过训练3DUnet模型和2DUnet结合方向场(2DUnetDF)模型, 利用多种模型各自优点进行分割结果的融合, 得到更好的泛化性能. 通过同中心和跨中心数据集的实验, 我们的方法能够有效提高新冠肺炎病灶分割的泛化性能, 为医学诊断分析提供帮助.
    1)  收稿日期 2021-05-20 录用日期 2021-05-20 Manuscript received May 20, 2021; accepted September 6, 2021 新一代人工智能重大项目(2018AAA0100400), 华中科技大学新型冠状病毒应急科技攻关专项(No.2020kfyXGYJ093, No.2020kfyXGYJ094, No.2020kfyXGYJ021), 国家自然科学基金(61703171)资助 Supported by the National Key Research and Development Program of China under Grant No. 2018AAA0100400, HUST COVID-19 Rapid Response Call (No. 2020kfyXGYJ093, No. 2020kfyXGYJ094, No.2020kfyXGYJ021), National Natural Science Foundation of China (No. 61703171) 本文责任编委 Recommended by Associate Editor 1. 华中科技大学, 电子信息与通信工程学院 武汉 430074 2. 华中科技大学, 同济医学院附属同济医院 武汉 430030 3. 华中科技大学, 同济医学院附属协和医院 武汉 430022 4. 华中科技大学, 人工智能与自动化学院 武汉 430074 1. School of Electronic Information and Communications,
    2)  Huazhong University of Science and Technology, Wuhan 4300742. Tongji Hospital affiliated Tongji Medical College, Huazhong University of Science and Technology Wuhan 430030 3. Union Hospital affiliated Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022 4. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074
  • 图  1  2DUnet, 3DUnet以及我们方法在交叉数据上的测试结果. 我们的方法提升了2D模型的检测精度,同时消除了3DUnet部分破碎的错误结果, 具有更好的分割性能.

    Fig.  1  An example of segmentation result on cross-datasets test by 2DUnet[34], 3DUnet[35] and our method. Our proposed method improves the 2D segmentation by detecting some lesions that 2D model does not segment and also eliminates scattered errors within 3DUnet segmentation. Our method obtains better segmentation and better generalization performance.

    图  2  新冠肺炎CT影像多模型融合自动分割整体流程. 我们利用区域增长的方式融合2D和3D模型的分割结果, 与单独一种模型相比取得了更好的效果

    Fig.  2  Pipeline of Fusion Multi-Models for Automatic COVID-19 Pneumonia Lesion Segmentation from CT Images. We fuse the 2D and 3D segmentations by region growing, achieving a better performance compared with the result of single model.

    图  3  2DUnet利用额外的方向场作为监督优化分割结果.

    Fig.  3  Illustration of 2DUnet with Direction field. 2DUnetDF uses an additional direction field branch to utilize the direction field to supervise and optimize the initial segmentation to get final segmentation.

    图  4  2D和3D分割结果融合方法. 我们使用2D分割结果作为种子, 同时3D结果作为相邻元素.我们检查种子点的八个相邻像素是否应该放入融合结果.

    Fig.  4  Illustration of 2D and 3D fusion method. We set 2D segmentation as seed pixels and 3D segmentation as neighboring pixels. We check the seed pixel’s eight neighboring pixels to see whether they need to be put in the fused result.

    图  5  在同中心数据集与多中心数据集上, 分析比较2DUnet, 3DUnet以及我们方法的分割结果.我们的方法减少了破碎区域, 同时得到更精确的分割结果.

    Fig.  5  Visual comparison of the COVID-19 pneumonia lesions segmentation results by 2DUnet, 3DUnet and our method on in-dataset and cross-datasets. Our method reduces the broken areas and gets a more precise segmentation.

    图  6  FMM能够更为准确地分割出新冠肺炎病灶边界.

    Fig.  6  FMM obtains better lesion boundary. Red contour indicates the predicted result.

    图  7  消融实验可视化定性比较方向场以及融合模块对于整体方法的贡献.

    Fig.  7  Visualization of the ablation result by different methods to analyze contribution of the direction field and fusion method. Red contour indicates the predicted result.

    图  8  一些分割结果有误的例子. FMM对于很小的病灶分割能力有一定局限.

    Fig.  8  Some failure cases. FMM doses not perform well in segmenting very small regions. Red contour indicates the predicted result.

    表  1  XH150训练, 分别在XH40和TJ40测试的Dice结果

    Table  1  Dice comparison of methods trained on XH150 and test on XH40 and TJ40 datasets.

    MethodXH40TJ40
    3DUnet80.9377.95
    2DUnet86.9474.64
    3D+2DUnet(FMM)87.5781.90
    3D+2DUnetDF(FMM)87.9382.90
    下载: 导出CSV

    表  2  TJ145训练, 分别在XH40和TJ40测试的Dice结果

    Table  2  Dice comparison of methods trained on TJ145 and test on XH40 and TJ40 datasets.

    MethodXH40TJ40
    3DUnet70.1779.81
    2DUnet78.2587.08
    3D+2DUnet(FMM)80.9387.52
    3D+2DUnetDF(FMM)82.4387.62
    下载: 导出CSV

    表  3  不同方法在同数据集测试的Hausdorff95结果

    Table  3  In-dataset evaluation of different methods for Hausdorff95

    MethodXH40TJ40
    3DUnet4.035.59
    2DUnet2.734.17
    3D+2DUnet(FMM)2.744.19
    3D+2DUnetDF(FMM)2.724.24
    下载: 导出CSV

    表  4  不同方法在交叉数据集测试的Hausdorff95结果

    Table  4  Cross-dataset evaluation of different methods for Hausdorff95

    MethodXH40TJ40
    3DUnet5.395.69
    2DUnet3.464.68
    3D+2DUnet(FMM)3.384.48
    3D+2DUnetDF(FMM)3.234.46
    下载: 导出CSV

    表  5  XH150训练, 不同融合方法分别在XH40和TJ40测试Dice的对比结果

    Table  5  Dice comparison of methods trained on XH150 test on XH40 and TJ40 datasets with different fusion methods.

    MethodXH40TJ40
    3DUnet80.9377.95
    3DUnet-Voting81.5579.10
    2DUnet86.9474.64
    2DUnet-Voting86.8172.49
    3D+2DUnet(FMM)87.5781.90
    Multi3D2DUnet-Voting85.0574.35
    Multi3D2DUnet-Voting(FMM)88.1982.36
    下载: 导出CSV

    表  6  TJ145训练, 不同融合方法分别在XH40和TJ40测试Dice的对比结果

    Table  6  Dice comparison of methods trained on TJ145 test on XH40 and TJ40 datasets with different fusion methods.

    MethodXH40TJ40
    3DUnet70.1779.81
    3DUnet-Voting73.8880.54
    2DUnet78.2587.08
    2DUnet-Voting80.9487.16
    3D+2DUnet(FMM)80.9387.52
    Multi3D2DUnet-Voting82.5186.73
    Multi3D2DUnet-Voting(FMM)85.4887.48
    下载: 导出CSV

    表  7  XH150训练, 分别在XH40和TJ40上测试的消融实验结果.

    Table  7  Ablation studies for Dice trained on XH150 test on XH40 and TJ40 datasets.

    MethodXH40TJ40
    3DUnet80.9377.95
    2DUnet86.9474.64
    2DUnetDF87.8376.84
    3D+2DUnet(FMM)87.5781.90
    3D+2DUnetDF(FMM)87.9382.90
    下载: 导出CSV

    表  8  TJ145训练, 分别在XH40和TJ40上测试的消融实验结果.

    Table  8  Ablation studies for Dice trained on TJ145 test on XH40 and TJ40 datasets.

    MethodXH40TJ40
    3DUnet70.1779.81
    2DUnet78.2587.08
    2DUnetDF80.5287.57
    3D+2DUnet(FMM)80.9387.52
    3D+2DUnetDF(FMM)82.4387.62
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-20
  • 录用日期:  2021-09-06
  • 网络出版日期:  2021-09-26

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