  This work presents a novel framework CISFA (Contrastive Image synthesis and
Self-supervised Feature Adaptation)that builds on image domain translation and
unsupervised feature adaptation for cross-modality biomedical image
segmentation. Different from existing works, we use a one-sided generative
model and add a weighted patch-wise contrastive loss between sampled patches of
the input image and the corresponding synthetic image, which serves as shape
constraints. Moreover, we notice that the generated images and input images
share similar structural information but are in different modalities. As such,
we enforce contrastive losses on the generated images and the input images to
train the encoder of a segmentation model to minimize the discrepancy between
paired images in the learned embedding space. Compared with existing works that
rely on adversarial learning for feature adaptation, such a method enables the
encoder to learn domain-independent features in a more explicit way. We
extensively evaluate our methods on segmentation tasks containing CT and MRI
images for abdominal cavities and whole hearts. Experimental results show that
the proposed framework not only outputs synthetic images with less distortion
of organ shapes, but also outperforms state-of-the-art domain adaptation
methods by a large margin.
