  Fluoroscopy is the standard imaging modality used to guide hip surgery and is
therefore a natural sensor for computer-assisted navigation. In order to
efficiently solve the complex registration problems presented during
navigation, human-assisted annotations of the intraoperative image are
typically required. This manual initialization interferes with the surgical
workflow and diminishes any advantages gained from navigation. We propose a
method for fully automatic registration using annotations produced by a neural
network. Neural networks are trained to simultaneously segment anatomy and
identify landmarks in fluoroscopy. Training data is obtained using an
intraoperatively incompatible 2D/3D registration of hip anatomy. Ground truth
2D labels are established using projected 3D annotations. Intraoperative
registration couples an intensity-based strategy with annotations inferred by
the network and requires no human assistance. Ground truth labels were obtained
in 366 fluoroscopic images across 6 cadaveric specimens. In a
leave-one-subject-out experiment, networks obtained mean dice coefficients for
left and right hemipelves, left and right femurs of 0.86, 0.87, 0.90, and 0.84.
The mean 2D landmark error was 5.0 mm. The pelvis was registered within 1
degree for 86% of the images when using the proposed intraoperative approach
with an average runtime of 7 seconds. In comparison, an intensity-only approach
without manual initialization, registered the pelvis to 1 degree in 18% of
images. We have created the first accurately annotated, non-synthetic, dataset
of hip fluoroscopy. By using these annotations as training data for neural
networks, state of the art performance in fluoroscopic segmentation and
landmark localization was achieved. Integrating these annotations allows for a
robust, fully automatic, and efficient intraoperative registration during
fluoroscopic navigation of the hip.
