Segmentation of X-ray Images by 3D-2D Registration Based on Multibody Physics

X-ray imaging is commonly used in clinical routine. In radiotherapy, spatial information is extracted from X-ray images to correctly position patients before treatment. Similarly, orthopedic surgeons assess the positioning and migration of implants after Total Hip Replacement (THR) with X-ray images. However, the projective nature of X-ray imaging hinders the reliable extraction of rigid structures in X-ray images, such as bones or metallic components. We developed an approach based on multibody physics that simultaneously registers multiple 3D shapes with one or more 2D X-ray images. Considered as physical bodies, shapes are driven by image forces, which exploit image gradient, and constraints, which enforce spatial dependencies between shapes. Our method was tested on post-operative radiographs of THR and thoroughly validated with gold standard datasets. The final target registration error was in average \(0.3\pm 0.16\) mm and the capture range improved more than 40 % with respect to reference registration methods.