Surgical treatment of complicated knee fractures is guided by real-time imaging using a mobile C-arm. Immediate and continuous control is achieved via 2D anatomy-specific standard views that correspond to a specific C-arm pose relative to the patient positioning, which is currently determined manually, following a trial-and-error approach at the cost of time and radiation dose. The characteristics of the standard views of the knee suggests that the shape information of individual bones could guide an automatic positioning procedure, reducing time and the amount of unnecessary radiation during C-arm positioning. To fully automate the C-arm positioning task during knee surgeries, we propose a complete framework that enables (1) automatic laterality and standard view classification and (2) automatic shape-based pose regression toward the desired standard view based on a single initial X-ray. A suitable shape representation is proposed to incorporate semantic information into the pose regression pipeline. The pipeline is designed to handle two distinct standard views simultaneously. Experiments were conducted to assess the performance of the proposed system on 3528 synthetic and 1386 real X-rays for the a.-p. and lateral standard. The view/laterality classificator resulted in an accuracy of 100\%/98\% on the simulated and 99\%/98\% on the real X-rays. The pose regression performance was $d\theta_{a.-p}=5.8\pm3.3\degree,\,d\theta_{lateral}=3.7\pm2.0\degree$ on the simulated data and $d\theta_{a.-p}=7.4\pm5.0\degree,\,d\theta_{lateral}=8.4\pm5.4\degree$ on the real data outperforming intensity-based pose regression.

翻译：复杂膝关节骨折的手术治疗依赖于移动C形臂的实时影像引导。当前，通过二维解剖特异性标准视图实现即时连续控制，这些视图对应C形臂相对于患者体位的特定姿态，目前需通过人工试错法确定，耗时且增加辐射剂量。膝关节标准视图的特性表明，骨骼个体的形状信息可指导自动定位流程，从而减少C形臂定位时间及不必要的辐射暴露。为实现膝关节手术中C形臂定位任务的完全自动化，我们提出一个完整框架，该框架具备：(1) 自动侧别与标准视图分类，(2) 基于单张初始X光片的形状姿态回归至目标标准视图。我们设计了一种合适的形状表征方法，将语义信息融入姿态回归流程，该流程可同时处理两种不同的标准视图。通过3528张合成X光片与1386张真实X光片（含前后位与侧位标准视图）的实验评估系统性能。视图/侧别分类器在模拟数据上达100%/98%准确率，在真实X光片上达99%/98%。姿态回归性能在模拟数据上为$d\theta_{a.-p}=5.8\pm3.3\degree,\,d\theta_{lateral}=3.7\pm2.0\degree$，在真实数据上为$d\theta_{a.-p}=7.4\pm5.0\degree,\,d\theta_{lateral}=8.4\pm5.4\degree$，优于基于强度的姿态回归方法。