论文标题
从延迟增强心脏MRI自动评估心肌梗死的级联框架
Cascaded Framework for Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI
论文作者
论文摘要
心肌和病理学的自动评估在对心肌梗塞患者的定量分析中起着重要作用。在本文中，我们提出了一个级联的卷积神经网络框架，用于延迟增强心脏MRI中的心肌梗塞分割和分类。具体来说，我们首先使用2D U-NET来细分整个心脏，包括左心室和心肌。然后，我们全心全意作为感兴趣的地区（ROI）。最后，使用新的2D U-NET来分割整个心脏ROI中的违规和无元区域。分割方法可以应用于分类任务，在分类任务中，违规或无回流区域的分割结果分类为病理病例。我们的方法在MICCAI 2020 EMIDEC分割任务中排名第二，骰子得分为86.28％，62.24％和77.76％，分别为92％的心肌，违规和NO-REFFLE区域，并在分类任务中排名第一。
Automatic evaluation of myocardium and pathology plays an important role in the quantitative analysis of patients suffering from myocardial infarction. In this paper, we present a cascaded convolutional neural network framework for myocardial infarction segmentation and classification in delayed-enhancement cardiac MRI. Specifically, we first use a 2D U-Net to segment the whole heart, including the left ventricle and the myocardium. Then, we crop the whole heart as a region of interest (ROI). Finally, a new 2D U-Net is used to segment the infraction and no-reflow areas in the whole heart ROI. The segmentation method can be applied to the classification task where the segmentation results with the infraction or no-reflow areas are classified as pathological cases. Our method took second place in the MICCAI 2020 EMIDEC segmentation task with Dice scores of 86.28%, 62.24%, and 77.76% for myocardium, infraction, and no-reflow areas, respectively, and first place in the classification task with an accuracy of 92%.
