Real time 2D/3D registration via CNN regression
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.
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)