Deep learning-based X-ray inpainting for improving spinal 2D-3D registration.
Int J Med Robot
; 17(2): e2228, 2021 Apr.
Article
in En
| MEDLINE
| ID: mdl-33462965
ABSTRACT
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Deep Learning
Limits:
Humans
Language:
En
Journal:
Int J Med Robot
Year:
2021
Document type:
Article
Affiliation country: