  Registration of 3D anatomic structures to their 2D dual fluoroscopic X-ray
images is a widely used motion tracking technique. However, deep learning
implementation is often impeded by a paucity of medical images and ground
truths. In this study, we proposed a transfer learning strategy for 3D-to-2D
registration using deep neural networks trained from an artificial dataset.
Digitally reconstructed radiographs (DRRs) and radiographic skull landmarks
were automatically created from craniocervical CT data of a female subject.
They were used to train a residual network (ResNet) for landmark detection and
a cycle generative adversarial network (GAN) to eliminate the style difference
between DRRs and actual X-rays. Landmarks on the X-rays experiencing GAN style
translation were detected by the ResNet, and were used in triangulation
optimization for 3D-to-2D registration of the skull in actual dual-fluoroscope
images (with a non-orthogonal setup, point X-ray sources, image distortions,
and partially captured skull regions). The registration accuracy was evaluated
in multiple scenarios of craniocervical motions. In walking, learning-based
registration for the skull had angular/position errors of 3.9 +- 2.1 deg / 4.6
+- 2.2 mm. However, the accuracy was lower during functional neck activity, due
to overly small skull regions imaged on the dual fluoroscopic images at
end-range positions. The methodology to strategically augment artificial
training data can tackle the complicated skull registration scenario, and has
potentials to extend to widespread registration scenarios.
