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Automatic segmentation of left ventricle (LV) myocardium in cardiacshort-axis cine MR images acquired on subjects with myocardial infarction is achallenging task, mainly because of the various types of image inhomogeneitycaused by the infarctions. Among the approaches proposed to automate the LVmyocardium segmentation task, methods based upon deep convolutional neuralnetworks (CNN) have demonstrated their exceptional accuracy and robustness inrecent years. However, most of the CNN-based approaches treat the frames in acardiac cycle independently, which fails to capture the valuable dynamics ofheart 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 ona ResNet-56 CNN, LV-related image features in consecutive frames of a cardiaccycle are extracted at both the low- and high-resolution levels, which areprocessed by the corresponding multi-level ConvLSTM models to generate themyocardium segmentations. A leave-one-out experiment was carried out on a setof 3,600 cardiac cine MR slices collected in-house for 8 porcine subjects withsurgically induced myocardial infarction. Compared with a solely CNN-basedapproach, the proposed approach demonstrated its superior robustness againstimage inhomogeneity by incorporating information from adjacent frames. It alsooutperformed a one-level ConvLSTM approach thanks to its capabilities to takeadvantage of image features at multiple resolution levels.

A multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR images