Surgical reduction of pelvic dislocation is a challenging procedure with poor long-term prognosis if reduction does not accurately restore natural morphology. The procedure often requires long fluoroscopic exposure times and trial-and-error to achieve accurate reduction. We report a method to automatically compute the target pose of dislocated bones in preoperative CT and provide 3D guidance of reduction using routine 2D fluoroscopy. A pelvic statistical shape model (SSM) and a statistical pose model (SPM) were formed from an atlas of 40 pelvic CT images. Multi-body bone segmentation was achieved by mapping the SSM to a preoperative CT via an active shape model. The target reduction pose for the dislocated bone is estimated by fitting the poses of undislocated bones to the SPM. Intraoperatively, multiple bones are registered to fluoroscopy images via 3D–2D registration to obtain 3D pose estimates from 2D images. The method was examined in three studies: (1) a simulation study of...

中文翻译：

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多体 3D-2D 配准用于骨科创伤手术中骨盆脱位的图像引导复位。

骨盆脱位的手术复位是一项具有挑战性的手术，如果复位不能准确恢复自然形态，则长期预后较差。该程序通常需要较长的透视曝光时间和反复试验才能实现准确的还原。我们报告了一种在术前 CT 中自动计算脱位骨骼的目标姿势并使用常规 2D 透视提供 3D 复位指导的方法。骨盆统计形状模型 (SSM) 和统计姿势模型 (SPM) 由 40 个骨盆 CT 图像的图谱形成。多体骨分割是通过主动形状模型将 SSM 映射到术前 CT 来实现的。通过将未脱位骨骼的姿势拟合到 SPM 来估计脱位骨骼的目标减少姿势。术中，通过 3D-2D 配准将多个骨骼配准到透视图像，以从 2D 图像获得 3D 姿势估计。该方法在三项研究中进行了检验：（1）模拟研究...