Analysis and modeling of the ventricles and myocardium are important in the
diagnostic and treatment of heart diseases. Manual delineation of those tissues
in cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of the
boundaries makes the segmentation task rather challenging. Furthermore, the
annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI,
are often not available. We propose an end-to-end segmentation framework based
on convolutional neural network (CNN) and adversarial learning. A dilated
residual U-shape network is used as a segmentor to generate the prediction
mask; meanwhile, a CNN is utilized as a discriminator model to judge the
segmentation quality. To leverage the available annotations across modalities
per patient, a new loss function named weak domain-transfer loss is introduced
to the pipeline. The proposed model is evaluated on the public dataset released
by the challenge organizer in MICCAI 2019, which consists of 45 sets of
multi-sequence CMR images. We demonstrate that the proposed adversarial
pipeline outperforms baseline deep-learning methods.Comment: 9 pages, 4 figures, conferenc