  In this paper, we present a Convolutional Neural Network (CNN) regression
approach for real-time 2-D/3-D registration. Different from optimization-based
methods, which iteratively optimize the transformation parameters over a
scalar-valued metric function representing the quality of the registration, the
proposed method exploits the information embedded in the appearances of the
Digitally Reconstructed Radiograph and X-ray images, and employs CNN regressors
to directly estimate the transformation parameters. The CNN regressors are
trained for local zones and applied in a hierarchical manner to break down the
complex regression task into simpler sub-tasks that can be learned separately.
Our experiment results demonstrate the advantage of the proposed method in
computational efficiency with negligible degradation of registration accuracy
compared to intensity-based methods.
