  Deep Learning-based 2D/3D registration enables fast, robust, and accurate
X-ray to CT image fusion when large annotated paired datasets are available for
training. However, the need for paired CT volume and X-ray images with ground
truth registration limits the applicability in interventional scenarios. An
alternative is to use simulated X-ray projections from CT volumes, thus
removing the need for paired annotated datasets. Deep Neural Networks trained
exclusively on simulated X-ray projections can perform significantly worse on
real X-ray images due to the domain gap. We propose a self-supervised 2D/3D
registration framework combining simulated training with unsupervised feature
and pixel space domain adaptation to overcome the domain gap and eliminate the
need for paired annotated datasets. Our framework achieves a registration
accuracy of 1.83$\pm$1.16 mm with a high success ratio of 90.1% on real X-ray
images showing a 23.9% increase in success ratio compared to reference
annotation-free algorithms.
