  Coronary artery disease (CAD) is the most common cause of death globally, and
its diagnosis is usually based on manual myocardial segmentation of Magnetic
Resonance Imaging (MRI) sequences. As the manual segmentation is tedious,
time-consuming and with low applicability, automatic myocardial segmentation
using machine learning techniques has been widely explored recently. However,
almost all the existing methods treat the input MRI sequences independently,
which fails to capture the temporal information between sequences, e.g., the
shape and location information of the myocardium in sequences along time. In
this paper, we propose a myocardial segmentation framework for sequence of
cardiac MRI (CMR) scanning images of left ventricular cavity, right ventricular
cavity, and myocardium. Specifically, we propose to combine conventional
networks and recurrent networks to incorporate temporal information between
sequences to ensure temporal consistent. We evaluated our framework on the
Automated Cardiac Diagnosis Challenge (ACDC) dataset. Experiment results
demonstrate that our framework can improve the segmentation accuracy by up to
2% in Dice coefficient.
