Automatic registration with continuous pose updates for marker-less
  surgical navigation in spine surgery
Florentin Liebmann Marco von Atzigen Dominik Stütz Julian Wolf Lukas Zingg Daniel Suter Laura Leoty Hooman Esfandiari Jess G. Snedeker Martin R. Oswald Marc Pollefeys Mazda Farshad Philipp Fürnstahl
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.