  Automatic and accurate segmentation of the ventricles and myocardium from
multi-sequence 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, \emph{e.g.,} bSSFP, LGE, and T2-weighted. Our
segmentation network is attention U-Net with pyramid pooling module, where
multi-level feature space and output space adversarial learning are proposed to
transfer discriminative domain knowledge across different datasets. Moreover,
we further introduce a group-wise feature recalibration module to enforce the
fine-grained semantic-level feature alignment that matching features from
different networks but with the same class label. We evaluate our method on the
multi-sequence 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.
