  Unsupervised domain adaptation (UDA) methods have shown their promising
performance in the cross-modality medical image segmentation tasks. These
typical methods usually utilize a translation network to transform images from
the source domain to target domain or train the pixel-level classifier merely
using translated source images and original target images. However, when there
exists a large domain shift between source and target domains, we argue that
this asymmetric structure could not fully eliminate the domain gap. In this
paper, we present a novel deep symmetric architecture of UDA for medical image
segmentation, which consists of a segmentation sub-network, and two symmetric
source and target domain translation sub-networks. To be specific, based on two
translation sub-networks, we introduce a bidirectional alignment scheme via a
shared encoder and private decoders to simultaneously align features 1) from
source to target domain and 2) from target to source domain, which helps
effectively mitigate the discrepancy between domains. Furthermore, for the
segmentation sub-network, we train a pixel-level classifier using not only
original target images and translated source images, but also original source
images and translated target images, which helps sufficiently leverage the
semantic information from the images with different styles. Extensive
experiments demonstrate that our method has remarkable advantages compared to
the state-of-the-art methods in both cross-modality Cardiac and BraTS
segmentation tasks.
