  We introduce a one-shot segmentation method to alleviate the burden of manual
annotation for medical images. The main idea is to treat one-shot segmentation
as a classical atlas-based segmentation problem, where voxel-wise
correspondence from the atlas to the unlabelled data is learned. Subsequently,
segmentation label of the atlas can be transferred to the unlabelled data with
the learned correspondence. However, since ground truth correspondence between
images is usually unavailable, the learning system must be well-supervised to
avoid mode collapse and convergence failure. To overcome this difficulty, we
resort to the forward-backward consistency, which is widely used in
correspondence problems, and additionally learn the backward correspondences
from the warped atlases back to the original atlas. This cycle-correspondence
learning design enables a variety of extra, cycle-consistency-based supervision
signals to make the training process stable, while also boost the performance.
We demonstrate the superiority of our method over both deep learning-based
one-shot segmentation methods and a classical multi-atlas segmentation method
via thorough experiments.
