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首页> 外文期刊>IEEE Transactions on Medical Imaging >Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
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Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

机译：用于MRI心脏自动多结构分割和诊断的深度学习技术：问题是否解决？

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摘要

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.
机译：从心脏磁共振图像（多层2D电影MRI）描绘左心室，心肌和右心室是建立诊断的常见临床任务。因此，在过去的几十年中，相应任务的自动化一直是深入研究的主题。在本文中，我们介绍了“自动心脏诊断挑战”数据集（ACDC），这是用于心脏MRI（CMR）评估的最大的可公开获得且具有完整注释的数据集。该数据集包含来自150个多设备CMRI记录的数据，以及来自两名医学专家的参考测量值和分类。本文的首要目标是衡量最先进的深度学习方法在评估CMRI方面的作用，即对心肌和两个心室进行分割以及对病理进行分类。在2017年MICCAI-ACDC挑战赛之后，我们报告了由深度学习方法的结果，该方法由9个研究小组提供的细分任务和4个研究组提供的分类任务。结果表明，最好的方法忠实地再现了专家分析的结果，自动提取临床指标的相关得分的平均值为0.97，自动诊断的准确性为0.96。这些结果显然为心脏CMRI的高度准确和全自动分析打开了方便之门。我们还确定了深度学习方法仍然失败的场景。数据集和详细结果均可在线在线获得，而该平台将保持开放状态以接受新提交。

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  • 来源
    《IEEE Transactions on Medical Imaging》 |2018年第11期|2514-2525|共12页
  • 作者

    Olivier Bernard; Alain Lalande; Clement Zotti; Frederick Cervenansky; Xin Yang; Pheng-Ann Heng; Irem Cetin; Karim Lekadir; Oscar Camara; Miguel Angel Gonzalez Ballester; Gerard Sanroma; Sandy Napel; Steffen Petersen; Georgios Tziritas; Elias Grinias; Mahendra Khened; Var;

  • 作者单位

    University of Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, University of Lyon 1, Lyon, France;

    Le2i Laboratory, CNRS FRE 2005, University of Burgundy, Dijon, France;

    Computer Science Department, University of Sherbrooke, Sherbrooke, Canada;

    University of Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, University of Lyon 1, Lyon, France;

    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong;

    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong;

    Barcelona Centre for New Medical Technologies, Universitat Pompeu Fabra, Barcelona, Spain;

    Barcelona Centre for New Medical Technologies, Universitat Pompeu Fabra, Barcelona, Spain;

    Barcelona Centre for New Medical Technologies, Universitat Pompeu Fabra, Barcelona, Spain;

    Barcelona Centre for New Medical Technologies, Universitat Pompeu Fabra, Barcelona, Spain;

    Barcelona Centre for New Medical Technologies, Universitat Pompeu Fabra, Barcelona, Spain;

    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA;

    William Harvey Research Institute, Queen Mary University of London, London, U.K.;

    Department of Computer Science, University of Crete, Heraklion, Greece;

    Department of Computer Science, University of Crete, Heraklion, Greece;

    Department of Engineering Design, IIT Madras, Chennai, India;

    Department of Engineering Design, IIT Madras, Chennai, India;

    Department of Engineering Design, IIT Madras, Chennai, India;

    Inria-Asclepios Project, Sophia Antipolis, France;

    Inria-Asclepios Project, Sophia Antipolis, France;

    Inria-Asclepios Project, Sophia Antipolis, France;

    Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany;

    Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany;

    Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany;

    Department of Computer Science, Mannheim University of Applied Sciences, Mannheim, Germany;

    Department of Computer Science, Mannheim University of Applied Sciences, Mannheim, Germany;

    Department of Computer Science, Mannheim University of Applied Sciences, Mannheim, Germany;

    Computer Vision Laboratory, ETH Zürich, Zürich, Switzerland;

    Computer Vision and Geometry Group, ETH Zürich, Zürich, Switzerland;

    Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands;

    Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands;

    Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Seoul, South Korea;

    Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Seoul, South Korea;

    Qure.ai company, Mumbai, India;

    Qure.ai company, Mumbai, India;

    TIRO-UMR E 4320 Laboratory, University of Nice, Nice, France;

    Computer Science Department, University of Sherbrooke, Sherbrooke, Canada;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Machine learning; Magnetic resonance imaging; Myocardium; Image segmentation; Task analysis; Biomedical imaging; Heart;

    机译：机器学习;磁共振成像;心肌;图像分割;任务分析;生物医学成像;心脏;

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