DeepAI AI Chat
Log In Sign Up

Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration

by   Robert Grupp, et al.

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. We propose a method for fully automatic registration using annotations produced by a neural network. Neural networks are trained to simultaneously segment anatomy and identify landmarks in fluoroscopy. Training data is obtained using an intraoperatively incompatible 2D/3D registration of hip anatomy. Ground truth 2D labels are established using projected 3D annotations. Intraoperative registration couples an intensity-based strategy with annotations inferred by the network and requires no human assistance. Ground truth labels were obtained in 366 fluoroscopic images across 6 cadaveric specimens. In a leave-one-subject-out experiment, networks obtained mean dice coefficients for left and right hemipelves, left and right femurs of 0.86, 0.87, 0.90, and 0.84. The mean 2D landmark error was 5.0 mm. The pelvis was registered within 1 degree for 86 with an average runtime of 7 seconds. In comparison, an intensity-only approach without manual initialization, registered the pelvis to 1 degree in 18 images. We have created the first accurately annotated, non-synthetic, dataset of hip fluoroscopy. By using these annotations as training data for neural networks, state of the art performance in fluoroscopic segmentation and landmark localization was achieved. Integrating these annotations allows for a robust, fully automatic, and efficient intraoperative registration during fluoroscopic navigation of the hip.


page 3

page 5

page 8

page 14


Fully Automatic Segmentation of 3D Brain Ultrasound: Learning from Coarse Annotations

Intra-operative ultrasound is an increasingly important imaging modality...

Localizing dexterous surgical tools in X-ray for image-based navigation

X-ray image based surgical tool navigation is fast and supplies accurate...

Patch-Based Image Similarity for Intraoperative 2D/3D Pelvis Registration During Periacetabular Osteotomy

Periacetabular osteotomy is a challenging surgical procedure for treatin...

BIRL: Benchmark on Image Registration methods with Landmark validation

This report presents a generic image registration benchmark with automat...

Pose-dependent weights and Domain Randomization for fully automatic X-ray to CT Registration

Fully automatic X-ray to CT registration requires a solid initialization...

High-resolution Image Registration of Consecutive and Re-stained Sections in Histopathology

We compare variational image registration in consectutive and re-stained...

Code Repositories


Code and data for the "annotation" component of the IPCAI 2020 paper: "Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration." or

view repo


Code for the registration component of the IPCAI 2020 paper: "Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration." or

view repo