学习用于膝关节骨X射线到CT配准的密集2D-3D对应关系
Learning Dense 2D-3D Correspondence for X-ray-to-CT Registration of Knee Bones
- KU Leuven（鲁汶大学）
- AZ Delta（AZ Delta医院）
- Arthro ID
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究如何从X光片恢复膝关节骨6自由度姿势，提出学习密集2D-3D对应关系的方法，由投影几何监督，用共享权重模型训练，能对未见患者配准，姿势通过特定求解得出，在解剖学上有语义且泛化性好。
AI中文摘要：
在给定患者术前CT分割的情况下，从普通X光片中恢复膝关节骨的6自由度姿势，可将常规低剂量图像转化为关节几何形状的定量测量，无需重复CT或固定双平面设备的额外剂量。经典方法将渲染的骨轮廓与图像边缘对齐，近期方法通过可微X射线渲染器反向传播图像相似性损失来优化姿势，但两者每次仅处理一名患者且单视图下脆弱。本文学习了一种摊销的、与个体无关的密集2D-3D对应关系，仅由投影几何监督。每个骨一个共享权重模型，在758名患者上训练，可对训练中未见的患者进行配准。姿势通过全局、无需初始化、无需渲染的PnP+RANSAC求解以封闭形式得出。由于X射线形成是透射的，对应目标是透射感知的而非绑定到单个表面。该表示在解剖学上具有语义，在大型单机构队列中模型对未见患者泛化良好。
英文摘要：
Recovering the 6-DoF pose of the knee bones from a plain radiograph, given the patient's segmented pre-operative CT, turns a routine low-dose image into a quantitative measurement of joint geometry, without the added dose of a repeat CT or a fixed biplanar rig. Classic solutions align a rendered bone silhouette to image edges; recent alternatives refine pose by backpropagating an image-similarity loss through a differentiable X-ray renderer. Both operate one patient at a time and are fragile under a single view. Silhouettes are depth-ambiguous, and differentiable-rendering refinement has a narrow capture range at substantial per-iteration cost. We instead learn an amortized, subject-agnostic dense 2D-3D correspondence, supervised solely by projection geometry. One shared-weight model per bone, trained across 758 patients, registers patients unseen during training. The pose then follows in closed form from a global, initialization-free, render-free PnP+RANSAC solve. Because X-ray formation is transmissive, our correspondence target is transmission-aware rather than tied to a single surface. Though trained only to register, the representation is anatomically semantic: a simple classifier reads a landmark's anatomical region from its embedding across held-out patients, and the same features separate the knee's bones into a 2D-3D-consistent identity learned without any bone label. On a large single-institution cohort the model generalizes well to held-out patients.
