  Accurate segmentation of cardiac structures can assist doctors to diagnose
diseases, and to improve treatment planning, which is highly demanded in the
clinical practice. However, the shortage of annotation and the variance of the
data among different vendors and medical centers restrict the performance of
advanced deep learning methods. In this work, we present a fully automatic
method to segment cardiac structures including the left (LV) and right
ventricle (RV) blood pools, as well as for the left ventricular myocardium
(MYO) in MRI volumes. Specifically, we design a semi-supervised learning method
to leverage unlabelled MRI sequence timeframes by label propagation. Then we
exploit style transfer to reduce the variance among different centers and
vendors for more robust cardiac image segmentation. We evaluate our method in
the M&Ms challenge 7 , ranking 2nd place among 14 competitive teams.
