Convolutional neural network (CNN) based segmentation methods provide an
efficient and automated way for clinicians to assess the structure and function
of the heart in cardiac MR images. While CNNs can generally perform the
segmentation tasks with high accuracy when training and test images come from
the same domain (e.g. same scanner or site), their performance often degrades
dramatically on images from different scanners or clinical sites. We propose a
simple yet effective way for improving the network generalization ability by
carefully designing data normalization and augmentation strategies to
accommodate common scenarios in multi-site, multi-scanner clinical imaging data
sets. We demonstrate that a neural network trained on a single-site
single-scanner dataset from the UK Biobank can be successfully applied to
segmenting cardiac MR images across different sites and different scanners
without substantial loss of accuracy. Specifically, the method was trained on a
large set of 3,975 subjects from the UK Biobank. It was then directly tested on
600 different subjects from the UK Biobank for intra-domain testing and two
other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2
scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). The
proposed method produces promising segmentation results on the UK Biobank test
set which are comparable to previously reported values in the literature, while
also performing well on cross-domain test sets, achieving a mean Dice metric of
0.90 for the left ventricle, 0.81 for the myocardium and 0.82 for the right
ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the
myocardium on the BSCMR-AS dataset. The proposed method offers a potential
solution to improve CNN-based model generalizability for the cross-scanner and
cross-site cardiac MR image segmentation task.