  Anatomical and biophysical modeling of left atrium (LA) and proximal
pulmonary veins (PPVs) is important for clinical management of several cardiac
diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA
and PPVs through visualization. However, there is a strong need for an advanced
image segmentation method to be applied to cardiac MRI for quantitative
analysis of LA and PPVs. In this study, we address this unmet clinical need by
exploring a new deep learning-based segmentation strategy for quantification of
LA and PPVs with high accuracy and heightened efficiency. Our approach is based
on a multi-view convolutional neural network (CNN) with an adaptive fusion
strategy and a new loss function that allows fast and more accurate convergence
of the backpropagation based optimization. After training our network from
scratch by using more than 60K 2D MRI images (slices), we have evaluated our
segmentation strategy to the STACOM 2013 cardiac segmentation challenge
benchmark. Qualitative and quantitative evaluations, obtained from the
segmentation challenge, indicate that the proposed method achieved the
state-of-the-art sensitivity (90%), specificity (99%), precision (94%), and
efficiency levels (10 seconds in GPU, and 7.5 minutes in CPU).
