  Disentangled representation is a powerful technique to tackle domain shift
problem in medical image analysis in unsupervised domain adaptation
setting.However, previous methods only focus on exacting domain-invariant
feature and ignore whether exacted feature is meaningful for downstream
tasks.We propose a novel framework, called semantic-guided disentangled
representation (SGDR), an effective method to exact semantically meaningful
feature for segmentation task to improve performance of cross modality medical
image segmentation in unsupervised domain adaptation setting. To exact the
meaningful domain-invariant features of different modality, we introduce a
content discriminator to force the content representation to be embedded to the
same space and a feature discriminator to exact the meaningful
representation.We also use pixel-level annotations to guide the encoder to
learn features that are meaningful for segmentation task.We validated our
method on two public datasets and experiment results show that our approach
outperforms the state of the art methods on two evaluation metrics by a
significant margin.
