Related Experiment Video
Updated: Nov 20, 2025
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
Published on: November 23, 2019
Deep learning-based X-ray inpainting for improving spinal 2D-3D registration
Hooman Esfandiari1, Simon Weidert2, István Kövesházi2
1School of Biomedical Engineering, Surgical Technologies Lab, Centre for Hip Health and Mobility, University of British Columbia, Vancouver, British Columbia, Canada.
Background:
Two-dimensional (2D)-3D registration is challenging in the presence of implant projections on intraoperative images, which can limit the registration capture range. Here, we investigate the use of deep-learning-based inpainting for removing implant projections from the X-rays to improve the registration performance.
Methods:
We trained deep-learning-based inpainting models that can fill in the implant projections on X-rays. Clinical datasets were collected to evaluate the inpainting based on six image similarity measures. The effect of X-ray inpainting on capture range of 2D-3D registration was also evaluated.
Results:
The X-ray inpainting significantly improved the similarity between the inpainted images and the ground truth. When applying inpainting before the 2D-3D registration process, we demonstrated significant recovery of the capture range by up to 85%.
Conclusion:
Applying deep-learning-based inpainting on X-ray images masked by implants can markedly improve the capture range of the associated 2D-3D registration task.
