  One of the first steps in the diagnosis of most cardiac diseases, such as
pulmonary hypertension, coronary heart disease is the segmentation of
ventricles from cardiac magnetic resonance (MRI) images. Manual segmentation of
the right ventricle requires diligence and time, while its automated
segmentation is challenging due to shape variations and illdefined borders. We
propose a deep learning based method for the accurate segmentation of right
ventricle, which does not require post-processing and yet it achieves the
state-of-the-art performance of 0.86 Dice coefficient and 6.73 mm Hausdorff
distance on RVSC-MICCAI 2012 dataset. We use a novel adaptive cost function to
counter extreme class-imbalance in the dataset. We present a comprehensive
comparative study of loss functions, architectures, and ensembling techniques
to build a principled approach for biomedical segmentation tasks.
