  Supervised learning is well-known to fail at generalization under
distribution shifts. In typical clinical settings, the source data is
inaccessible and the target distribution is represented with a handful of
samples: adaptation can only happen at test time on a few or even a single
subject(s). We investigate test-time single-subject adaptation for
segmentation, and propose a Shape-guided Entropy Minimization objective for
tackling this task. During inference for a single testing subject, our loss is
minimized with respect to the batch normalization's scale and bias parameters.
We show the potential of integrating various shape priors to guide adaptation
to plausible solutions, and validate our method in two challenging scenarios:
MRI-to-CT adaptation of cardiac segmentation and cross-site adaptation of
prostate segmentation. Our approach exhibits substantially better performances
than the existing test-time adaptation methods. Even more surprisingly, it
fares better than state-of-the-art domain adaptation methods, although it
forgoes training on additional target data during adaptation. Our results
question the usefulness of training on target data in segmentation adaptation,
and points to the substantial effect of shape priors on test-time inference.
Our framework can be readily used for integrating various priors and for
adapting any segmentation network, and our code is available.
