  Myocardium segmentation of late gadolinium enhancement (LGE) Cardiac MR
images is important for evaluation of infarction regions in clinical practice.
The pathological myocardium in LGE images presents distinctive brightness and
textures compared with the healthy tissues, making it much more challenging to
be segment. Instead, the balanced-Steady State Free Precession (bSSFP) cine
images show clearly boundaries and can be easily segmented. Given this fact, we
propose a novel shape-transfer GAN for LGE images, which can 1) learn to
generate realistic LGE images from bSSFP with the anatomical shape preserved,
and 2) learn to segment the myocardium of LGE images from these generated
images. It's worth to note that no segmentation label of the LGE images is used
during this procedure. We test our model on dataset from the Multi-sequence
Cardiac MR Segmentation Challenge. The results show that the proposed
Shape-Transfer GAN can achieve accurate myocardium masks of LGE images.
