基于优化的刚性2D/3D配准基线：采用CMA-ES应用于脊柱手术导航
An Optimization-based Baseline for Rigid 2D/3D Registration Applied to Spine Surgical Navigation Using CMA-ES
AI总结：
针对骨科脊柱手术导航的2D/3D配准需求，提出基于CMA-ES算法的由粗到精配准框架，经临床数据验证有效，可作为学习方法的后处理，补充优化类配准方法体系。
摘要：
鲁棒且高效的基于优化的2D/3D配准框架对骨科手术机器人的导航系统至关重要，它能在术中提供手术器械和植入物的精确位置信息。尽管近年来人工智能技术发展迅速，传统的基于优化的配准方法在2D/3D配准领域仍不可或缺。该方法极高的精度使其可作为基于学习的方法的后处理步骤，从而为配准提供可靠保障。本文提出了一种基于CMA-ES（协方差矩阵自适应进化策略）算法的由粗到精的配准框架。我们利用脊柱不同部位的数据对所提方法进行了大量测试，结果表明该框架在真实骨科脊柱手术临床数据上具备有效性。本工作可视为对我们此前研究中所用基于优化的方法的补充拓展。
英文摘要：
A robust and efficient optimization-based 2D/3D registration framework is crucial for the navigation system of orthopedic surgical robots. It can provide precise position information of surgical instruments and implants during surgery. While artificial intelligence technology has advanced rapidly in recent years, traditional optimization-based registration methods remain indispensable in the field of 2D/3D registration.he exceptional precision of this method enables it to be considered as a post-processing step of the learning-based methods, thereby offering a reliable assurance for registration. In this paper, we present a coarse-to-fine registration framework based on the CMA-ES algorithm. We conducted intensive testing of our method using data from different parts of the spine. The results shows the effectiveness of the proposed framework on real orthopedic spine surgery clinical data. This work can be viewed as an additional extension that complements the optimization-based methods employed in our previous studies.
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