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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, 2023, 49(2): 317−328 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, 2023, 49(2): 317−328 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, 2023, 49(2): 317−328 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, 2023, 49(2): 317−328 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(2020kfyXGYJ093, 2020kfyXGYJ094, 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: chengfeng@hust.edu.cn

    李震:华中科技大学附属同济医院教授. 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:华中科技大学附属协和医院教授. 主要研究方向为放射诊断, 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. 本文通信作者. 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. 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 National Key Research and Development Program of China (2018AAA0100400), Covid-19 Rapid Response Call Project of Huazhong University of Science and Technology (2020kfyXGYJ093, 2020kfyXGYJ094, 2020kfyXGYJ021), National Natural Science Foundation of China (61703171)
More Information
    Author Bio:

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

    LI Zhen Professor at Tongji Hospital, Huazhong University of Science and Technology. His research interest covers abdominal imaging diagnosis, functional magnetic resonance imaging, and medical image analysis

    ZHENG Chuan-Sheng Professor at Union Hospital, Huazhong University of Science and Technology. 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 Info-rmation 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 Te-chnology. 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. 其中新型冠状病毒患者的计算机断层扫描(Computer 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新型冠状病毒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新型冠状病毒肺炎病灶分割算法具有重要意义. 提出一种新冠肺炎病灶多模型融合分割方法. 通过训练3DUnet模型和2DUnet结合方向场(Direction field, DF)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, 证明该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.
  • 图  1  2DUnet[34]、3DUnet[35]以及本文方法在交叉数据上的测试结果

    Fig.  1  An example of segmentation result on cross-datasets test by 2DUnet[34], 3DUnet[35] and our method

    图  2  新冠肺炎CT影像多模型融合自动分割整体流程

    Fig.  2  Pipeline of fusion multi-models for automatic Covid-19 pneumonia lesion segmentation from CT images

    图  3  2DUnet利用额外的方向场作为监督优化分割结果

    Fig.  3  2DUnet utilizes additional orientation fields as supervision to optimize segmentation results

    图  4  2D和3D分割结果融合方法

    Fig.  4  Illustration of 2D and 3D fusion method

    图  5  在同中心数据集与多中心数据集上, 分析比较2DUnet、3DUnet和本文方法的分割结果

    Fig.  5  Visual comparison of the Covid-19 pneumonia lesions segmentation results by 2DUnet, 3DUnet and our method on in-dataset and cross-dataset

    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

    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

    图  8  困难样本结果可视化

    Fig.  8  The visualization of hard samples

    表  1  XH150训练在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

    方法XH40TJ40
    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

    方法XH40TJ40
    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

    方法XH40TJ40
    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

    方法XH40TJ40
    3DUnet80.9377.95
    3DUnet-Voting81.5579.10
    2DUnet86.9474.64
    2DUnet-Voting86.8172.49
    3D + 2DUne (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

    方法XH40TJ40
    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

    方法XH40TJ40
    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

    方法XH40TJ40
    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
  • 刊出日期:  2023-02-20

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