Multimodality-based knee joint modelling method with bone and cartilage structures for total knee arthroplasty

OBJECTIVE We propose a robust and accurate knee joint modelling method with bone and cartilage structures to enable accurate surgical guidance for knee surgery. METHODS A multimodality registration strategy is proposed to fuse magnetic resonance (MR) and computed tomography (CT) images of the femur and tibia separately to remove spatial inconsistency caused by knee bending in CT/MR scans. Automatic segmentation of the femur, tibia and cartilages is carried out with region of interest clustering and intensity analysis based on the multimodal fusion of images. RESULTS Experimental results show that the registration error is 1.13 ± 0.30 mm. The Dice similarity coefficient values of the proposed segmentation method of the femur, tibia, femoral and tibial cartilages are 0.969, 0.966, 0.910 and 0.872, respectively. CONCLUSIONS This study demonstrates the feasibility and effectiveness of multimodality-based registration and segmentation methods for knee joint modelling. The proposed method can provide users with 3D anatomical models of the femur, tibia, and cartilages with few human inputs.