title: A CNN Regression Approach for Real-Time 2D/3D Registration.
abstract: 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.
authors.lastname: Shun Miao
authors.forename: Z Jane
authors.initials: ZJ
authors.lastname: Wang
authors.lastname: Rui Liao
mesh_terms: D019645:Arthroplasty, Replacement, Knee; D017548:Echocardiography, Transesophageal; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D007719:Knee Joint / Q000000981:diagnostic imaging; D000069550:Machine Learning; D016571:Neural Networks, Computer; D011859:Radiography / Q000379:methods*; D012044:Regression Analysis