Established surgical navigation systems for pedicle screw placement have been
proven to be accurate, but still reveal limitations in registration or surgical
guidance. Registration of preoperative data to the intraoperative anatomy
remains a time-consuming, error-prone task that includes exposure to harmful
radiation. Surgical guidance through conventional displays has well-known
drawbacks, as information cannot be presented in-situ and from the surgeon's
perspective. Consequently, radiation-free and more automatic registration
methods with subsequent surgeon-centric navigation feedback are desirable. In
this work, we present an approach that automatically solves the registration
problem for lumbar spinal fusion surgery in a radiation-free manner. A deep
neural network was trained to segment the lumbar spine and simultaneously
predict its orientation, yielding an initial pose for preoperative models,
which then is refined for each vertebra individually and updated in real-time
with GPU acceleration while handling surgeon occlusions. An intuitive surgical
guidance is provided thanks to the integration into an augmented reality based
navigation system. The registration method was verified on a public dataset
with a mean of 96\% successful registrations, a target registration error of
2.73 mm, a screw trajectory error of 1.79{\deg} and a screw entry point error
of 2.43 mm. Additionally, the whole pipeline was validated in an ex-vivo
surgery, yielding a 100\% screw accuracy and a registration accuracy of 1.20
mm. Our results meet clinical demands and emphasize the potential of RGB-D data
for fully automatic registration approaches in combination with augmented
reality guidance.

本文提出了一种无辐射方法的自动注册方案，使用深度神经网络自动解决腰椎融合手术的注册问题，并结合增强现实导航系统提供直观的手术引导。在公开数据集上验证了注册方法并获得较高的注册成功率和精度，在离体手术中也取得了良好的结果，展现了 RGB-D 数据在自动注册方法和增强现实引导中的潜力。

脊柱手术无标记外科导航的连续姿态跟踪自动注册

Automatic registration with continuous pose updates for marker-less  surgical navigation in spine surgery

In surgical navigation, finding correspondence between preoperative plan and
intraoperative anatomy, the so-called registration task, is imperative. One
promising approach is to intraoperatively digitize anatomy and register it with
the preoperative plan. State-of-the-art commercial navigation systems implement
such approaches for pedicle screw placement in spinal fusion surgery. Although
these systems improve surgical accuracy, they are not gold standard in clinical
practice. Besides economical reasons, this may be due to their difficult
integration into clinical workflows and unintuitive navigation feedback.
Augmented Reality has the potential to overcome these limitations.
Consequently, we propose a surgical navigation approach comprising
intraoperative surface digitization for registration and intuitive holographic
navigation for pedicle screw placement that runs entirely on the Microsoft
HoloLens. Preliminary results from phantom experiments suggest that the method
may meet clinical accuracy requirements.

本研究介绍了一种使用增强现实技术的手术导航方法，涉及手术导航、注册任务、椎弓根螺丝置入等方面，使用 Microsoft HoloLens，通过对患者体表进行数字化，利用全息投影进行直观的导航反馈，初步实验表明该方法能够满足临床精度要求。