Multisequence Cardiac MR Segmentation with Adversarial Domain Adaptation Network Automatic and accurate segmentation of the ventricles and myocardium from multisequence cardiac MRI (CMR) is crucial for the diagnosis and treatment management for patients suffering from myocardial infarction (MI). However, due to the existence of domain shift among different modalities of datasets, the performance of deep neural networks drops significantly when the training and testing datasets are distinct. In this paper, we propose an unsupervised domain alignment method to explicitly alleviate the domain shifts among different modalities of CMR sequences, e.g., bSSFP, LGE, and T2weighted. Our segmentation network is attention UNet with pyramid pooling module, where multilevel feature space and output space adversarial learning are proposed to transfer discriminative domain knowledge across different datasets. Moreover, we further introduce a groupwise feature recalibration module to enforce the finegrained semanticlevel feature alignment that matching features from different networks but with the same class label. We evaluate our method on the multisequence cardiac MR Segmentation Challenge 2019 datasets, which contain three different modalities of MRI sequences. Extensive experimental results show that the proposed methods can obtain significant segmentation improvements compared with the baseline models.

Adversarial Convolutional Networks with Weak DomainTransfer for Multisequence Cardiac MR Images Segmentation 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 timeconsuming. 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 endtoend segmentation framework based on convolutional neural network (CNN) and adversarial learning. A dilated residual Ushape 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 domaintransfer 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 multisequence CMR images. We demonstrate that the proposed adversarial pipeline outperforms baseline deeplearning methods.