Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
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DOI：
10.1109/tmi.2018.2837502
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发表时间：
2018-11-01
影响因子：
10.6
通讯作者：
Jodoin, Pierre-Marc
中科院分区：
文献类型：
--
作者：
Bernard, Olivier;Lalande, Alain;Jodoin, Pierre-Marc
从心脏磁共振图像（多层二维MRI）描绘左心室腔、心肌和右心室是一项常见的临床诊断任务。因此，在过去的几十年里，相应任务的自动化一直是激烈研究的主题。在本文中，我们介绍了“心脏自动诊断挑战”数据集（ACDC），这是用于心脏MRI （CMR）评估的最大的公开可用且完全注释的数据集。该数据集包含来自150个多设备CMRI记录的数据，并附有两位医学专家的参考测量和分类。本文的总体目标是衡量最先进的深度学习方法在评估CMRI方面可以走多远，即分割心肌和两个心室以及分类病理。在2017年MICCAI-ACDC挑战之后，我们报告了由9个研究小组为分词任务提供的深度学习方法和4个研究小组为分类任务提供的深度学习方法的结果。结果表明，最佳方法忠实地再现了专家分析，导致临床指标自动提取的平均相关评分为0.97，自动诊断的准确率为0.96。这些结果清楚地为心脏CMRI的高精度和全自动分析打开了大门。我们还确定了深度学习方法仍然失败的场景。数据集和详细的结果都在网上公开，同时该平台将继续接受新的提交。
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.