Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration.

ABSTRACT:

Purpose

Fluoroscopy is the standard imaging modality used to guide hip surgery and is therefore a natural sensor for computer-assisted navigation. In order to efficiently solve the complex registration problems presented during navigation, human-assisted annotations of the intraoperative image are typically required. This manual initialization interferes with the surgical workflow and diminishes any advantages gained from navigation. In this paper, we propose a method for fully automatic registration using anatomical annotations produced by a neural network.

Methods

Neural networks are trained to simultaneously segment anatomy and identify landmarks in fluoroscopy. Training data are obtained using a computationally intensive, intraoperatively incompatible, 2D/3D registration

SUBMITTER: Grupp RB 

PROVIDER: S-EPMC7263976 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature