  We present a novel deep learning-based framework: Embedded Feature Similarity
Optimization with Specific Parameter Initialization (SOPI) for 2D/3D medical
image registration which is a most challenging problem due to the difficulty
such as dimensional mismatch, heavy computation load and lack of golden
evaluation standard. The framework we design includes a parameter specification
module to efficiently choose initialization pose parameter and a
fine-registration module to align images. The proposed framework takes
extracting multi-scale features into consideration using a novel composite
connection encoder with special training techniques. We compare the method with
both learning-based methods and optimization-based methods on a in-house
CT/X-ray dataset as well as simulated data to further evaluate performance. Our
experiments demonstrate that the method in this paper has improved the
registration performance, and thereby outperforms the existing methods in terms
of accuracy and running time. We also show the potential of the proposed method
as an initial pose estimator. The code is available at
https://github.com/m1nhengChen/SOPI
