  With the advent of Cardiac Cine Magnetic Resonance (CMR) Imaging, there has
been a paradigm shift in medical technology, thanks to its capability of
imaging different structures within the heart without ionizing radiation.
However, it is very challenging to conduct pre-operative planning of minimally
invasive cardiac procedures without accurate segmentation and identification of
the left ventricle (LV), right ventricle (RV) blood-pool, and LV-myocardium.
Manual segmentation of those structures, nevertheless, is time-consuming and
often prone to error and biased outcomes. Hence, automatic and computationally
efficient segmentation techniques are paramount. In this work, we propose a
novel memory-efficient Convolutional Neural Network (CNN) architecture as a
modification of both CondenseNet, as well as DenseNet for ventricular
blood-pool segmentation by introducing a bottleneck block and an upsampling
path. Our experiments show that the proposed architecture runs on the Automated
Cardiac Diagnosis Challenge (ACDC) dataset using half (50%) the memory
requirement of DenseNet and one-twelfth (~ 8%) of the memory requirements of
U-Net, while still maintaining excellent accuracy of cardiac segmentation. We
validated the framework on the ACDC dataset featuring one healthy and four
pathology groups whose heart images were acquired throughout the cardiac cycle
and achieved the mean dice scores of 96.78% (LV blood-pool), 93.46% (RV
blood-pool) and 90.1% (LV-Myocardium). These results are promising and promote
the proposed methods as a competitive tool for cardiac image segmentation and
clinical parameter estimation that has the potential to provide fast and
accurate results, as needed for pre-procedural planning and/or pre-operative
applications.
