  Intraoperative fluoroscopy is a frequently used modality in minimally
invasive orthopedic surgeries. Aligning the intraoperatively acquired X-ray
image with the preoperatively acquired 3D model of a computed tomography (CT)
scan reduces the mental burden on surgeons induced by the overlapping
anatomical structures in the acquired images. This paper proposes a fully
automatic registration method that is robust to extreme viewpoints and does not
require manual annotation of landmark points during training. It is based on a
fully convolutional neural network (CNN) that regresses the scene coordinates
for a given X-ray image. The scene coordinates are defined as the intersection
of the back-projected rays from a pixel toward the 3D model. Training data for
a patient-specific model were generated through a realistic simulation of a
C-arm device using preoperative CT scans. In contrast, intraoperative
registration was achieved by solving the perspective-n-point (PnP) problem with
a random sample and consensus (RANSAC) algorithm. Experiments were conducted
using a pelvic CT dataset that included several real fluoroscopic (X-ray)
images with ground truth annotations. The proposed method achieved an average
mean target registration error (mTRE) of 3.79 mm in the 50th percentile of the
simulated test dataset and projected mTRE of 9.65 mm in the 50th percentile of
real fluoroscopic images for pelvis registration.
