  The success of deep convolutional neural networks (DCNNs) benefits from high
volumes of annotated data. However, annotating medical images is laborious,
expensive, and requires human expertise, which induces the label scarcity
problem. Especially when encountering the domain shift, the problem becomes
more serious. Although deep unsupervised domain adaptation (UDA) can leverage
well-established source domain annotations and abundant target domain data to
facilitate cross-modality image segmentation and also mitigate the label
paucity problem on the target domain, the conventional UDA methods suffer from
severe performance degradation when source domain annotations are scarce. In
this paper, we explore a challenging UDA setting - limited source domain
annotations. We aim to investigate how to efficiently leverage unlabeled data
from the source and target domains with limited source annotations for
cross-modality image segmentation. To achieve this, we propose a new
label-efficient UDA framework, termed MT-UDA, in which the student model
trained with limited source labels learns from unlabeled data of both domains
by two teacher models respectively in a semi-supervised manner. More
specifically, the student model not only distills the intra-domain semantic
knowledge by encouraging prediction consistency but also exploits the
inter-domain anatomical information by enforcing structural consistency.
Consequently, the student model can effectively integrate the underlying
knowledge beneath available data resources to mitigate the impact of source
label scarcity and yield improved cross-modality segmentation performance. We
evaluate our method on MM-WHS 2017 dataset and demonstrate that our approach
outperforms the state-of-the-art methods by a large margin under the
source-label scarcity scenario.
