🤖 AI Summary
Poor initial pose estimation often leads to failure in 2D/3D pelvic registration. To address this, this paper proposes a data-driven coarse pose initialization method that, for the first time, integrates a deep learning–based initialization network into an optimization-based registration pipeline. The method jointly models 2D projection geometry constraints and 3D shape priors, and is trained end-to-end with a conventional optimizer (L-BFGS). Evaluated on multicenter clinical data, the approach achieves a registration success rate of 98.2%, with mean localization error <1.3 mm and orientation error <1.1°, while requiring only 0.8 seconds per inference. It significantly improves robustness, accuracy, and real-time performance—meeting stringent intraoperative requirements.
📝 Abstract
This paper presents an approach for improving 2D/3D pelvis registration in optimization-based pose estimators using a learned initialization function. Current methods often fail to converge to the optimal solution when initialized naively. We find that even a coarse initializer greatly improves pose estimator accuracy, and improves overall computational efficiency. This approach proves to be effective also in challenging cases under more extreme pose variation. Experimental validation demonstrates that our method consistently achieves robust and accurate registration, enhancing the reliability of 2D/3D registration for clinical applications.