  Unsupervised domain adaptation (UDA) for cross-modality medical image
segmentation has shown great progress by domain-invariant feature learning or
image appearance translation. Adapted feature learning usually cannot detect
domain shifts at the pixel level and is not able to achieve good results in
dense semantic segmentation tasks. Image appearance translation, e.g. CycleGAN,
translates images into different styles with good appearance, despite its
population, its semantic consistency is hardly to maintain and results in poor
cross-modality segmentation. In this paper, we propose intra- and
cross-modality semantic consistency (ICMSC) for UDA and our key insight is that
the segmentation of synthesised images in different styles should be
consistent. Specifically, our model consists of an image translation module and
a domain-specific segmentation module. The image translation module is a
standard CycleGAN, while the segmentation module contains two domain-specific
segmentation networks. The intra-modality semantic consistency (IMSC) forces
the reconstructed image after a cycle to be segmented in the same way as the
original input image, while the cross-modality semantic consistency (CMSC)
encourages the synthesized images after translation to be segmented exactly the
same as before translation. Comprehensive experimental results on
cross-modality hip joint bone segmentation show the effectiveness of our
proposed method, which achieves an average DICE of 81.61% on the acetabulum and
88.16% on the proximal femur, outperforming other state-of-the-art methods. It
is worth to note that without UDA, a model trained on CT for hip joint bone
segmentation is non-transferable to MRI and has almost zero-DICE segmentation.
