Deep Learning-based Marker-less Pose Estimation of Interventional Tools using Surrogate Keypoints

Estimating the position of an intervention needle is an important ability in computer-assisted interventions. Currently, such pose estimations rely either on radiation-intensive CT imaging or need additional optical markers which add overhead to the clinical workflow. We propose a novel deep-learning-based technique for pose estimation of interventional tools which relies on detecting visible features on the tool itself without additional markers.We also propose a novel and fast pipeline for creating vast amounts of robustly labeled and markerless ground truth data for training such neural networks. Initial evaluations suggest that with needle base and needle tip localization errors of about 1 and 4 cm, Our approach can yield a search corridor that can be used to find the needle in a low-dose CT image, reducing radiation exposure.