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.
@article{arxiv.2008.01216,
title = {Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation},
author = {Hongwei Li and Jianguo Zhang and Bjoern Menze},
journal= {arXiv preprint arXiv:2008.01216},
year = {2020}
}
Comments
method description of our solution in M&M segmentation challenge, STACOM 2020