  State-of-the-art deep learning-based registration methods employ three
different learning strategies: supervised learning, which requires costly
manual annotations, unsupervised learning, which heavily relies on hand-crafted
similarity metrics designed by domain experts, or learning from synthetic data,
which introduces a domain shift. To overcome the limitations of these
strategies, we propose a novel self-supervised learning paradigm for
unsupervised registration, relying on self-training. Our idea is based on two
key insights. Feature-based differentiable optimizers 1) perform reasonable
registration even from random features and 2) stabilize the training of the
preceding feature extraction network on noisy labels. Consequently, we propose
cyclical self-training, where pseudo labels are initialized as the displacement
fields inferred from random features and cyclically updated based on more and
more expressive features from the learning feature extractor, yielding a
self-reinforcement effect. We evaluate the method for abdomen and lung
registration, consistently surpassing metric-based supervision and
outperforming diverse state-of-the-art competitors. Source code is available at
https://github.com/multimodallearning/reg-cyclical-self-train.
