护理研究2026，Vol.40Issue(10)：1693-1698,6.DOI:10.12102/j.issn.1009-6493.2026.10.011
基于生成对抗网络的椎骨关节约束配准方法研究
Research on the registration method with vertebral joint constraints based on generative adversarial networks
摘要
Abstract
Objective:To propose a 2D/3D medical image registration method based on generative adversarial network(GAN)for improving spatial alignment accuracy of images in minimally invasive spinal surgery navigation.Methods:A registration framework for spinal preoperative 3D CT and intraoperative 2D X-ray images was constructed based on the LIDC-IDRI dataset.Deformation parameters conforming to vertebral joint constraints were generated using a generative adversarial network.Digitally reconstructed radiographs technology was employed to project preoperative 3D CT data onto a 2D plane to generate synthetic X-ray images.A stage-wise parameter decoupling method was adopted for iterative parameter optimization.2D/3D medical image registration was achieved.The performance of the proposed algorithm was validated using Elastix as the benchmark method.Results:The GAN-based registration method outperformed the open-source medical image registration framework Elastix in terms of mean absolute error(MAE),normalized cross-correlation(NCC),and normalized mutual information(NMI).The GAN-based registration achieved an MAE that was only 52.4%of that of the open-source medical image registration framework Elastix,while its NCC and NMI were 10.2%and 42.8%higher than Elastix,respectively.Conclusions:By synergizing adversarial learning with gradient optimization,the GAN-based registration method achieves high-precision and robust intraoperative image registration.关键词
手术导航/2D/3D影像配准/生成式对抗网络/深度学习/参数优化Key words
surgical navigation/2D/3D image registration/generative adversarial networks/deep learning/parameter optimization引用本文复制引用
邢珍珍,颜立祥..基于生成对抗网络的椎骨关节约束配准方法研究[J].护理研究,2026,40(10):1693-1698,6.基金项目
2023年度山西省高等学校科技创新项目,编号:2023L352 （）
