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

CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-View CNN