Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation
This work addresses the problem of domain generalization in cardiac imaging for medical practitioners, but it is incremental as it builds on existing GAN and data augmentation methods.
The paper tackled domain shifts in cardiac image segmentation across multi-centre, multi-vendor datasets by proposing a generalisable framework using GANs with attention loss and stacked data augmentation, achieving average Dice scores of 90.3% for the left ventricle, 85.9% for the myocardium, and 86.5% for the right ventricle on a hidden validation set.
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.