  Test-time adaptation (TTA) refers to adapting a trained model to a new domain
during testing. Existing TTA techniques rely on having multiple test images
from the same domain, yet this may be impractical in real-world applications
such as medical imaging, where data acquisition is expensive and imaging
conditions vary frequently. Here, we approach such a task, of adapting a
medical image segmentation model with only a single unlabeled test image. Most
TTA approaches, which directly minimize the entropy of predictions, fail to
improve performance significantly in this setting, in which we also observe the
choice of batch normalization (BN) layer statistics to be a highly important
yet unstable factor due to only having a single test domain example. To
overcome this, we propose to instead integrate over predictions made with
various estimates of target domain statistics between the training and test
statistics, weighted based on their entropy statistics. Our method, validated
on 24 source/target domain splits across 3 medical image datasets surpasses the
leading method by 2.9% Dice coefficient on average.
