A CNN Regression Approach for Real Time 2D/3D Registration
In this paper, we present a Convolutional Neural Network (CNN) regression approach to address the two major limitations of existing intensity based 2 D/3 D registration technology: 1) slow computation and 2) small capture range. 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. An automatic feature extraction step is introduced to calculate 3 D pose indexed features that are sensitive to the variables to be regressed while robust to other factors. The CNN regressors are then trained for local zones and applied in a hierarchical manner to break down the complex regression task into multiple simpler sub tasks that can be learned separately. Weight sharing is furthermore employed in the CNN regression model to reduce the memory footprint. The proposed approach has been quantitatively evaluated on 3 potential clinical applications, demonstrating its significant advantage in providing highly accurate real time 2 D/3 D registration with a significantly enlarged capture range when compared to intensity based methods.
IEEE Transactions on Medical Imaging