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

• URL: http://arxiv.org/abs/2207.10996v1
• Date: Fri, 22 Jul 2022 10:30:00 GMT
• Title: Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration
• Authors: Zachary MC Baum, Yipeng Hu, Dean C Barratt
• Abstract summary: This work formulates image registration as a meta-learning algorithm. Experiments are presented in this paper using clinical transrectal ultrasound image data from 108 prostate cancer patients.
• Score: 0.37501702548174964
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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