Introducing Learning Rate Adaptation CMA-ES into Rigid Intraoperative 2D/3D Registration for Spinal Surgery

The covariance matrix adaptive evolution strategy (CMA-ES) has been widely used in the field of 2D/3D registration in recent years. This optimization method exhibits exceptional robustness and usability for complex surgical scenarios. However, due to the inherent ill-posed nature of the 2D/3D registration task and the presence of numerous local minima in the landscape of similarity measures, evolution strategies often require a larger population size in each generation to ensure the stability of registration and the globality and effectiveness of search, which makes the entire process computationally expensive. In this paper, we build a 2D/3D registration framework based on a learning rate adaptation CMA-ES manner. The framework employs a fixed and small population size, leading to minimized runtime and optimal utilization of computing resources. We conduct experimental comparisons between the proposed framework and other intensity-based baselines using a substantial volume of synthetic data. The results suggest that our method demonstrates superiority in both registration accuracy and running time compared with other optimization-based baselines. Code is available at github.com/m1nhengChen/CMAES-reg.