Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In The international journal of medical robotics + computer assisted surgery : MRCAS

BACKGROUND : 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. This article is protected by copyright. All rights reserved.

Esfandiari Hooman, Weidert Simon, Kövesházi István, Anglin Carolyn, Street John, Hodgson Antony J


2D-3D registration, Capture range, Convolutional neural network, Deep learning, Inpainting, Medical image registration, Pedicle screw, Spine, X-ray