  Neural networks have been proposed for medical image registration by
learning, with a substantial amount of training data, the optimal
transformations between image pairs. These trained networks can further be
optimized on a single pair of test images - known as test-time optimization.
This work formulates image registration as a meta-learning algorithm. Such
networks can be trained by aligning the training image pairs while
simultaneously improving test-time optimization efficacy; tasks which were
previously considered two independent training and optimization processes. The
proposed meta-registration is hypothesized to maximize the efficiency and
effectiveness of the test-time optimization in the "outer" meta-optimization of
the networks. For image guidance applications that often are time-critical yet
limited in training data, the potentially gained speed and accuracy are
compared with classical registration algorithms, registration networks without
meta-learning, and single-pair optimization without test-time optimization
data. Experiments are presented in this paper using clinical transrectal
ultrasound image data from 108 prostate cancer patients. These experiments
demonstrate the effectiveness of a meta-registration protocol, which yields
significantly improved performance relative to existing learning-based methods.
Furthermore, the meta-registration achieves comparable results to classical
iterative methods in a fraction of the time, owing to its rapid test-time
optimization process.
