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Analysis and modeling of the ventricles and myocardium are important in thediagnostic and treatment of heart diseases. Manual delineation of those tissuesin cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of theboundaries makes the segmentation task rather challenging. Furthermore, theannotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI,are often not available. We propose an end-to-end segmentation framework basedon convolutional neural network (CNN) and adversarial learning. A dilatedresidual U-shape network is used as a segmentor to generate the predictionmask; meanwhile, a CNN is utilized as a discriminator model to judge thesegmentation quality. To leverage the available annotations across modalitiesper patient, a new loss function named weak domain-transfer loss is introducedto the pipeline. The proposed model is evaluated on the public dataset releasedby the challenge organizer in MICCAI 2019, which consists of 45 sets ofmulti-sequence CMR images. We demonstrate that the proposed adversarialpipeline outperforms baseline deep-learning methods.

Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-Sequence Cardiac MR Images Segmentation