  In this work, we present a fully automatic method to segment cardiac
structures from late-gadolinium enhanced (LGE) images without using labelled
LGE data for training, but instead by transferring the anatomical knowledge and
features learned on annotated balanced steady-state free precession (bSSFP)
images, which are easier to acquire. Our framework mainly consists of two
neural networks: a multi-modal image translation network for style transfer and
a cascaded segmentation network for image segmentation. The multi-modal image
translation network generates realistic and diverse synthetic LGE images
conditioned on a single annotated bSSFP image, forming a synthetic LGE training
set. This set is then utilized to fine-tune the segmentation network
pre-trained on labelled bSSFP images, achieving the goal of unsupervised LGE
image segmentation. In particular, the proposed cascaded segmentation network
is able to produce accurate segmentation by taking both shape prior and image
appearance into account, achieving an average Dice score of 0.92 for the left
ventricle, 0.83 for the myocardium, and 0.88 for the right ventricle on the
test set.
