  In this paper, we propose a new deep learning framework for an automatic
myocardial infarction evaluation from clinical information and delayed
enhancement-MRI (DE-MRI). The proposed framework addresses two tasks. The first
task is automatic detection of myocardial contours, the infarcted area, the
no-reflow area, and the left ventricular cavity from a short-axis DE-MRI
series. It employs two segmentation neural networks. The first network is used
to segment the anatomical structures such as the myocardium and left
ventricular cavity. The second network is used to segment the pathological
areas such as myocardial infarction, myocardial no-reflow, and normal
myocardial region. The segmented myocardium region from the first network is
further used to refine the second network's pathological segmentation results.
The second task is to automatically classify a given case into normal or
pathological from clinical information with or without DE-MRI. A cascaded
support vector machine (SVM) is employed to classify a given case from its
associated clinical information. The segmented pathological areas from DE-MRI
are also used for the classification task. We evaluated our method on the 2020
EMIDEC MICCAI challenge dataset. It yielded an average Dice index of 0.93 and
0.84, respectively, for the left ventricular cavity and the myocardium. The
classification from using only clinical information yielded 80% accuracy over
five-fold cross-validation. Using the DE-MRI, our method can classify the cases
with 93.3% accuracy. These experimental results reveal that the proposed method
can automatically evaluate the myocardial infarction.
