Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration

TL;DR

This paper introduces a meta-learning approach for image registration that enhances test-time optimization, leading to faster and more accurate registration, especially useful in time-critical medical imaging scenarios with limited training data.

Contribution

It formulates image registration as a meta-learning problem, enabling simultaneous training and optimization, which improves test-time efficiency and accuracy over existing methods.

Findings

Meta-registration outperforms existing learning-based registration methods.

It achieves comparable accuracy to classical methods with significantly reduced computation time.

The approach is validated on clinical ultrasound data from prostate cancer patients.

Abstract

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.