Towards automation in non-invasive measurement of knee implant displacement

Abstract

Non-invasive measurement of knee implant loosening is important to provide a diagnostic tool for patients with recurrent complaints after a total knee arthroplasty (TKA). Displacement measurements are currently estimated between tibial implant and bone using a loading device, CT imaging and an advanced 3D image analysis workflow. However, user interaction is required within each step of this workflow, especially in the segmentation of implant and bone, increasing the complexity of this task and affecting its reproducibility. A deep learning-based segmentation model can alleviate the workload by increasing automation and reducing the variability of manual segmentation. In this work, we propose a segmentation algorithm for the tibial implant and tibial bone cortex. The automatically obtained segmentations are then introduced in the displacement calculation workflow and four displacement measurements are calculated, namely mean target registration error (mTRE), maximum total point motion (MTPM), magnitude of translation and rotation. Results show that the parameter distributions are similar to the manual approach, with intra-class correlation values ranging from 0.96 to 0.99 for the different displacement measurements. Moreover, the methodological error has a smaller or comparable distribution, showing the feasibility to increase automation in knee implant displacement assessment.