School of Biomedical Engineering, Surgical Technologies Lab, Centre for Hip Health and Mobility, University of British Columbia, Vancouver, British Columbia, Canada.
Department for General, Trauma and Reconstructive Surgery, LMU Munich, Munich, Germany.
Int J Med Robot. 2021 Apr;17(2):e2228. doi: 10.1002/rcs.2228. Epub 2021 Feb 15.
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.
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.
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%.
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.
在术中图像存在植入物投影的情况下，二维（2D）-三维（3D）配准具有挑战性，这可能会限制配准的捕获范围。在这里，我们研究了基于深度学习的图像修复技术在去除 X 光片中的植入物投影以提高配准性能的应用。
我们训练了基于深度学习的图像修复模型，可以在 X 光片中填充植入物投影。收集了临床数据集，以基于六种图像相似性度量来评估图像修复。还评估了 X 射线修复对 2D-3D 配准捕获范围的影响。
X 射线修复显著提高了修复图像与真实图像之间的相似性。在将修复应用于 2D-3D 配准过程之前，我们证明了捕获范围的显著恢复，最高可达 85%。
在被植入物遮挡的 X 光片上应用基于深度学习的图像修复技术可以显著提高相关 2D-3D 配准任务的捕获范围。
