  Domain adaptation (DA) has drawn high interests for its capacity to adapt a
model trained on labeled source data to perform well on unlabeled or weakly
labeled target data from a different domain. Most common DA techniques require
the concurrent access to the input images of both the source and target
domains. However, in practice, it is common that the source images are not
available in the adaptation phase. This is a very frequent DA scenario in
medical imaging, for instance, when the source and target images come from
different clinical sites. We propose a novel formulation for adapting
segmentation networks, which relaxes such a constraint. Our formulation is
based on minimizing a label-free entropy loss defined over target-domain data,
which we further guide with a domain invariant prior on the segmentation
regions. Many priors can be used, derived from anatomical information. Here, a
class-ratio prior is learned via an auxiliary network and integrated in the
form of a Kullback-Leibler (KL) divergence in our overall loss function. We
show the effectiveness of our prior-aware entropy minimization in adapting
spine segmentation across different MRI modalities. Our method yields
comparable results to several state-of-the-art adaptation techniques, even
though is has access to less information, the source images being absent in the
adaptation phase. Our straight-forward adaptation strategy only uses one
network, contrary to popular adversarial techniques, which cannot perform
without the presence of the source images. Our framework can be readily used
with various priors and segmentation problems.
