  Background: Cardiac MRI derived biventricular mass and function parameters,
such as end-systolic volume (ESV), end-diastolic volume (EDV), ejection
fraction (EF), stroke volume (SV), and ventricular mass (VM) are clinically
well established. Image segmentation can be challenging and time-consuming, due
to the complex anatomy of the human heart.
  Objectives: This study introduces $\nu$-net (/nju:n$\varepsilon$t/) -- a deep
learning approach allowing for fully-automated high quality segmentation of
right (RV) and left ventricular (LV) endocardium and epicardium for extraction
of cardiac function parameters.
  Methods: A set consisting of 253 manually segmented cases has been used to
train a deep neural network. Subsequently, the network has been evaluated on 4
different 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 simple
adjustment procedure, allowing for the adaptation to distinct segmentation
philosophies. $\nu$-net exhibits state of-the-art performance in terms of dice
coefficient.
  Conclusions: Biventricular mass and function parameters can be determined
reliably in high quality by applying a deep neural network for cardiac MRI
segmentation, especially in the anatomically complex right ventricle. Adaption
to individual segmentation styles by applying a simple adjustment procedure is
viable, allowing for the processing of novel data without time-consuming
additional training.
