  Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnosis in
many heart related diseases. Recently, deep neural networks have demonstrated
successful automatic segmentation, thus alleviating the burden of
time-consuming manual contouring of cardiac structures. Moreover, frameworks
such as nnU-Net provide entirely automatic model configuration to unseen
datasets enabling out-of-the-box application even by non-experts. However,
current studies commonly neglect the clinically realistic scenario, in which a
trained network is applied to data from a different domain such as deviating
scanners or imaging protocols. This potentially leads to unexpected performance
drops of deep learning models in real life applications. In this work, we
systematically study challenges and opportunities of domain transfer across
images from multiple clinical centres and scanner vendors. In order to maintain
out-of-the-box usability, we build upon a fixed U-Net architecture configured
by the nnU-net framework to investigate various data augmentation techniques
and batch normalization layers as an easy-to-customize pipeline component and
provide general guidelines on how to improve domain generalizability abilities
in existing deep learning methods. Our proposed method ranked first at the
Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge
(M&Ms).
