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Fully automatic X-ray to CT registration requires a solid initialization to provide an initial alignment within the capture range of existing intensity-based registrations. This work addresses that need by providing a novel automatic initialization, which enables end to end registration. First, a neural network is trained once to detect a set of anatomical landmarks on simulated X-rays. A domain randomization scheme is proposed to enable the network to overcome the challenge of being trained purely on simulated data and run inference on real X-rays. Then, for each patient CT, a fully-automatic patient-specific landmark extraction scheme is used. It is based on backprojecting and clustering the previously trained network's predictions on a set of simulated X-rays. Next, the network is retrained to detect the new landmarks. Finally the combination of network and 3D landmark locations is used to compute the initialization using a perspective-n-point algorithm. During the computation of the pose, a weighting scheme is introduced to incorporate the confidence of the network in detecting the landmarks. The algorithm is evaluated on the pelvis using both real and simulated x-rays. The mean (&#x00B1; standard deviation) target registration error in millimetres is 4.1 &#x00B1; 4.3 for simulated X-rays with a success rate of 92% and 4.2 &#x00B1; 3.9 for real X-rays with a success rate of 86.8%, where a success is defined as a translation error of less than 30 <i>mm</i>.

Pose-Dependent Weights and Domain Randomization for Fully Automatic X-Ray to CT Registration