Deep learning-based X-ray inpainting for improving spinal 2D-3D registration

Esfandiari, Hooman; Weidert, Simon; Kövesházi, Istvan; Anglin, Carolyn; Street, John und Hodgson, Antony J. (2021): Deep learning-based X-ray inpainting for improving spinal 2D-3D registration. In: International Journal of Medical Robotics and Computer Assisted Surgery, Bd. 17, Nr. 2, e2228

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.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 1478-5951
• Sprache: Englisch