  Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from
a labeled source domain to an unlabeled and unseen target domain, which is
usually trained on data from both domains. Access to the source domain data at
the adaptation stage, however, is often limited, due to data storage or privacy
issues. To alleviate this, in this work, we target source free UDA for
segmentation, and propose to adapt an ``off-the-shelf" segmentation model
pre-trained in the source domain to the target domain, with an adaptive
batch-wise normalization statistics adaptation framework. Specifically, the
domain-specific low-order batch statistics, i.e., mean and variance, are
gradually adapted with an exponential momentum decay scheme, while the
consistency of domain shareable high-order batch statistics, i.e., scaling and
shifting parameters, is explicitly enforced by our optimization objective. The
transferability of each channel is adaptively measured first from which to
balance the contribution of each channel. Moreover, the proposed source free
UDA framework is orthogonal to unsupervised learning methods, e.g.,
self-entropy minimization, which can thus be simply added on top of our
framework. Extensive experiments on the BraTS 2018 database show that our
source free UDA framework outperformed existing source-relaxed UDA methods for
the cross-subtype UDA segmentation task and yielded comparable results for the
cross-modality UDA segmentation task, compared with a supervised UDA methods
with the source data.
