Shape-based pose estimation for automatic standard views of the knee
This work addresses a specific problem for surgeons and patients by automating C-arm positioning in knee surgeries to reduce time and radiation dose, representing an incremental improvement over existing methods.
The paper tackles the problem of manually determining C-arm poses for knee surgery standard views, which is time-consuming and increases radiation exposure, by proposing an automatic framework that uses shape-based pose estimation from a single X-ray, achieving high classification accuracy (up to 100%) and low pose errors (e.g., 3.7±2.0° on simulated data).
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 $dθ_{a.-p}=5.8\pm3.3\degree,\,dθ_{lateral}=3.7\pm2.0\degree$ on the simulated data and $dθ_{a.-p}=7.4\pm5.0\degree,\,dθ_{lateral}=8.4\pm5.4\degree$ on the real data outperforming intensity-based pose regression.