  Tackling domain shifts in multi-centre and multi-vendor data sets remains
challenging for cardiac image segmentation. In this paper, we propose a
generalisable segmentation framework for cardiac image segmentation in which
multi-centre, multi-vendor, multi-disease datasets are involved. A generative
adversarial networks with an attention loss was proposed to translate the
images from existing source domains to a target domain, thus to generate
good-quality synthetic cardiac structure and enlarge the training set. A stack
of data augmentation techniques was further used to simulate real-world
transformation to boost the segmentation performance for unseen domains.We
achieved an average Dice score of 90.3% for the left ventricle, 85.9% for the
myocardium, and 86.5% for the right ventricle on the hidden validation set
across four vendors. We show that the domain shifts in heterogeneous cardiac
imaging datasets can be drastically reduced by two aspects: 1) good-quality
synthetic data by learning the underlying target domain distribution, and 2)
stacked classical image processing techniques for data augmentation.
