In this paper, we develop a 2D and 3D segmentation pipelines for fully
automated cardiac MR image segmentation using Deep Convolutional Neural
Networks (CNN). Our models are trained end-to-end from scratch using the ACD
Challenge 2017 dataset comprising of 100 studies, each containing Cardiac MR
images in End Diastole and End Systole phase. We show that both our
segmentation models achieve near state-of-the-art performance scores in terms
of distance metrics and have convincing accuracy in terms of clinical
parameters. A comparative analysis is provided by introducing a novel dice loss
function and its combination with cross entropy loss. By exploring different
network structures and comprehensive experiments, we discuss several key
insights to obtain optimal model performance, which also is central to the
theme of this challenge.