论文标题
从延迟增强心脏MRI的自动心肌梗死分段的级联卷积神经网络
Cascaded Convolutional Neural Network for Automatic Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRI
论文作者
论文摘要
自动分割心肌轮廓和相关区域（如违规和无元流量）是对心肌梗塞进行定量评估的重要一步。在这项工作中，我们提出了一个级联的卷积神经网络，用于延迟增强心脏MRI的自动心肌梗塞分割。我们首先使用2D U-NET专注于切片内信息以执行初步分割。之后，我们使用3D U-NET利用体积空间信息进行细微的分割。我们的方法在MICCAI 2020 EMIDEC挑战数据集上进行了评估，并在心肌，梗塞和无元素的平均骰子得分为0.8786、0.7124和0.7851上，均优胜所有其他分割竞赛的团队。
Automatic segmentation of myocardial contours and relevant areas like infraction and no-reflow is an important step for the quantitative evaluation of myocardial infarction. In this work, we propose a cascaded convolutional neural network for automatic myocardial infarction segmentation from delayed-enhancement cardiac MRI. We first use a 2D U-Net to focus on the intra-slice information to perform a preliminary segmentation. After that, we use a 3D U-Net to utilize the volumetric spatial information for a subtle segmentation. Our method is evaluated on the MICCAI 2020 EMIDEC challenge dataset and achieves average Dice score of 0.8786, 0.7124 and 0.7851 for myocardium, infarction and no-reflow respectively, outperforms all the other teams of the segmentation contest.
