  Although deep convolutional networks have reached state-of-the-art
performance in many medical image segmentation tasks, they have typically
demonstrated poor generalisation capability. To be able to generalise from one
domain (e.g. one imaging modality) to another, domain adaptation has to be
performed. While supervised methods may lead to good performance, they require
to fully annotate additional data which may not be an option in practice. In
contrast, unsupervised methods don't need additional annotations but are
usually unstable and hard to train. In this work, we propose a novel
weakly-supervised method. Instead of requiring detailed but time-consuming
annotations, scribbles on the target domain are used to perform domain
adaptation. This paper introduces a new formulation of domain adaptation based
on structured learning and co-segmentation. Our method is easy to train, thanks
to the introduction of a regularised loss. The framework is validated on
Vestibular Schwannoma segmentation (T1 to T2 scans). Our proposed method
outperforms unsupervised approaches and achieves comparable performance to a
fully-supervised approach.
