  Accurate segmentation of brain tumors from multi-modal Magnetic Resonance
(MR) images is essential in brain tumor diagnosis and treatment. However, due
to the existence of domain shifts among different modalities, the performance
of networks decreases dramatically when training on one modality and performing
on another, e.g., train on T1 image while performing on T2 image, which is
often required in clinical applications. This also prohibits a network from
being trained on labeled data and then transferred to unlabeled data from a
different domain. To overcome this, unsupervised domain adaptation (UDA)
methods provide effective solutions to alleviate the domain shift between
labeled source data and unlabeled target data. In this paper, we propose a
novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA
scheme. Specifically, a bidirectional image synthesis and segmentation module
is proposed to segment the brain tumor using the intermediate data
distributions generated for the two domains, which includes an image-to-image
translator and a shared-weighted segmentation network. Further, a
global-to-local consistency learning module is proposed to build robust
representation alignments in an integrated way. Extensive experiments on a
multi-modal brain MR benchmark dataset demonstrate that the proposed method
outperforms several state-of-the-art unsupervised domain adaptation methods by
a large margin, while a comprehensive ablation study validates the
effectiveness of each key component. The implementation code of our method will
be released at \url{https://github.com/KeleiHe/BiGL}.
