  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.
