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Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation

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Document pages: 8 pages

Abstract: 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.

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