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Background: Cardiac MRI derived biventricular mass and function parameters,such as end-systolic volume (ESV), end-diastolic volume (EDV), ejectionfraction (EF), stroke volume (SV), and ventricular mass (VM) are clinicallywell established. Image segmentation can be challenging and time-consuming, dueto the complex anatomy of the human heart.  Objectives: This study introduces $\nu$-net (/nju:n$\varepsilon$t/) -- a deeplearning approach allowing for fully-automated high quality segmentation ofright (RV) and left ventricular (LV) endocardium and epicardium for extractionof cardiac function parameters.  Methods: A set consisting of 253 manually segmented cases has been used totrain a deep neural network. Subsequently, the network has been evaluated on 4different multicenter data sets with a total of over 1000 cases.  Results: For LV EF the intraclass correlation coefficient (ICC) is 98, 95,and 80 % (95 %), and for RV EF 96, and 87 % (80 %) on the respective data sets(human expert ICCs reported in parenthesis). The LV VM ICC is 95, and 94 % (84%), and the RV VM ICC is 83, and 83 % (54 %). This study proposes a simpleadjustment procedure, allowing for the adaptation to distinct segmentationphilosophies. $\nu$-net exhibits state of-the-art performance in terms of dicecoefficient.  Conclusions: Biventricular mass and function parameters can be determinedreliably in high quality by applying a deep neural network for cardiac MRIsegmentation, especially in the anatomically complex right ventricle. Adaptionto individual segmentation styles by applying a simple adjustment procedure isviable, allowing for the processing of novel data without time-consumingadditional training.

$\nu$-net: Deep Learning for Generalized Biventricular Cardiac Mass and Function Parameters