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Tackling domain shifts in multi-centre and multi-vendor data sets remainschallenging for cardiac image segmentation. In this paper, we propose ageneralisable segmentation framework for cardiac image segmentation in whichmulti-centre, multi-vendor, multi-disease datasets are involved. A generativeadversarial networks with an attention loss was proposed to translate theimages from existing source domains to a target domain, thus to generategood-quality synthetic cardiac structure and enlarge the training set. A stackof data augmentation techniques was further used to simulate real-worldtransformation to boost the segmentation performance for unseen domains.Weachieved an average Dice score of 90.3% for the left ventricle, 85.9% for themyocardium, and 86.5% for the right ventricle on the hidden validation setacross four vendors. We show that the domain shifts in heterogeneous cardiacimaging datasets can be drastically reduced by two aspects: 1) good-qualitysynthetic data by learning the underlying target domain distribution, and 2)stacked classical image processing techniques for data augmentation.

Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation