  Unsupervised domain adaptation (UDA) between two significantly disparate
domains to learn high-level semantic alignment is a crucial yet challenging
task.~To this end, in this work, we propose exploiting low-level edge
information to facilitate the adaptation as a precursor task, which has a small
cross-domain gap, compared with semantic segmentation.~The precise contour then
provides spatial information to guide the semantic adaptation. More
specifically, we propose a multi-task framework to learn a contouring
adaptation network along with a semantic segmentation adaptation network, which
takes both magnetic resonance imaging (MRI) slice and its initial edge map as
input.~These two networks are jointly trained with source domain labels, and
the feature and edge map level adversarial learning is carried out for
cross-domain alignment. In addition, self-entropy minimization is incorporated
to further enhance segmentation performance. We evaluated our framework on the
BraTS2018 database for cross-modality segmentation of brain tumors, showing the
validity and superiority of our approach, compared with competing methods.
