  Patient-specific left ventricle (LV) myocardial models have the potential to
be used in a variety of clinical scenarios for improved diagnosis and treatment
plans. Cine cardiac magnetic resonance (MR) imaging provides high resolution
images to reconstruct patient-specific geometric models of the LV myocardium.
With the advent of deep learning, accurate segmentation of cardiac chambers
from cine cardiac MR images and unsupervised learning for image registration
for cardiac motion estimation on a large number of image datasets is
attainable. Here, we propose a deep leaning-based framework for the development
of patient-specific geometric models of LV myocardium from cine cardiac MR
images, using the Automated Cardiac Diagnosis Challenge (ACDC) dataset. We use
the deformation field estimated from the VoxelMorph-based convolutional neural
network (CNN) to propagate the isosurface mesh and volume mesh of the
end-diastole (ED) frame to the subsequent frames of the cardiac cycle. We
assess the CNN-based propagated models against segmented models at each cardiac
phase, as well as models propagated using another traditional nonrigid image
registration technique.
