  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.
