用学习的初始化提升骨盆2D/3D配准速度与鲁棒性
Regression-based Pelvic Pose Initialization for Fast and Robust 2D/3D Pelvis Registration
- 基于回归学习初始姿态，替代随机初始化
- 显著提升配准准确率，极端姿态下仍稳定
- 适合临床影像配准场景，提升系统可靠性
本文提出一种基于回归的骨盆姿态初始化方法，用于优化基于优化的2D/3D骨盆配准。现有方法在盲目初始化时常无法收敛至最优解。我们发现，即使粗略的初始姿态也能显著提升姿态估计精度，并提高整体计算效率。该方法在极端姿态变化的挑战性情况下依然有效。实验验证表明，本方法能持续实现鲁棒且精确的配准，提升了2D/3D配准在临床应用中的可靠性。
原文摘要 · Abstract (English)
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.
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