  Unsupervised domain adaptation (UDA) has been a vital protocol for migrating
information learned from a labeled source domain to facilitate the
implementation in an unlabeled heterogeneous target domain. Although UDA is
typically jointly trained on data from both domains, accessing the labeled
source domain data is often restricted, due to concerns over patient data
privacy or intellectual property. To sidestep this, we propose "off-the-shelf
(OS)" UDA (OSUDA), aimed at image segmentation, by adapting an OS segmentor
trained in a source domain to a target domain, in the absence of source domain
data in adaptation. Toward this goal, we aim to develop a novel batch-wise
normalization (BN) statistics adaptation framework. In particular, we gradually
adapt the domain-specific low-order BN statistics, e.g., mean and variance,
through an exponential momentum decay strategy, while explicitly enforcing the
consistency of the domain shareable high-order BN statistics, e.g., scaling and
shifting factors, via our optimization objective. We also adaptively quantify
the channel-wise transferability to gauge the importance of each channel, via
both low-order statistics divergence and a scaling factor.~Furthermore, we
incorporate unsupervised self-entropy minimization into our framework to boost
performance alongside a novel queued, memory-consistent self-training strategy
to utilize the reliable pseudo label for stable and efficient unsupervised
adaptation. We evaluated our OSUDA-based framework on both cross-modality and
cross-subtype brain tumor segmentation and cardiac MR to CT segmentation tasks.
Our experimental results showed that our memory consistent OSUDA performs
better than existing source-relaxed UDA methods and yields similar performance
to UDA methods with source data.
