Personalised pose estimation from single-plane moving fluoroscope images using deep convolutional neural networks.

ABSTRACT: Measuring joint kinematics is a key requirement for a plethora of biomechanical research and applications. While x-ray based systems avoid the soft-tissue artefacts arising in skin-based measurement systems, extracting the object's pose (translation and rotation) from the x-ray images is a time-consuming and expensive task. Based on about 106'000 annotated images of knee implants, collected over the last decade with our moving fluoroscope during activities of daily living, we trained a deep-learning model to automatically estimate the 6D poses for the femoral and tibial implant components. By pretraining a single stage of our architecture using renderings of the implant geometries, our approach offers personalised predictions of the implant poses, even for unseen subjects. Our approach pre

SUBMITTER: Vogl F 

PROVIDER: S-EPMC9231734 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature