  Automatic segmentation of left ventricle (LV) myocardium in cardiac
short-axis cine MR images acquired on subjects with myocardial infarction is a
challenging task, mainly because of the various types of image inhomogeneity
caused by the infarctions. Among the approaches proposed to automate the LV
myocardium segmentation task, methods based upon deep convolutional neural
networks (CNN) have demonstrated their exceptional accuracy and robustness in
recent years. However, most of the CNN-based approaches treat the frames in a
cardiac cycle independently, which fails to capture the valuable dynamics of
heart motion. Herein, an approach based on recurrent neural network (RNN),
specifically a multi-level convolutional long short-term memory (ConvLSTM)
model, is proposed to take the motion of the heart into consideration. Based on
a ResNet-56 CNN, LV-related image features in consecutive frames of a cardiac
cycle are extracted at both the low- and high-resolution levels, which are
processed by the corresponding multi-level ConvLSTM models to generate the
myocardium segmentations. A leave-one-out experiment was carried out on a set
of 3,600 cardiac cine MR slices collected in-house for 8 porcine subjects with
surgically induced myocardial infarction. Compared with a solely CNN-based
approach, the proposed approach demonstrated its superior robustness against
image inhomogeneity by incorporating information from adjacent frames. It also
outperformed a one-level ConvLSTM approach thanks to its capabilities to take
advantage of image features at multiple resolution levels.
