Luo Yi, Cao Wenming, He Zhiquan, Zou Wenlan, He Zhihai
Guangdong Multimedia Information Service Engineering Technology Research Center, Shenzhen University, Shenzhen, China.
Guangdong Multimedia Information Service Engineering Technology Research Center, Shenzhen University, Shenzhen, China; Video Processing and Communication Laboratory, Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA.
Comput Med Imaging Graph. 2021 Jul;91:101931. doi: 10.1016/j.compmedimag.2021.101931. Epub 2021 May 26.
Deformable medical image registration has the necessary value of theoretical research and clinical application. Traditional methods cannot meet clinical application standards in terms of registration accuracy and efficiency. This article proposes a deformable generate adversarial registration framework, which avoids the dependence on ground-truth deformation. The proposed residual registration network based on Nested U-Net has excellent feature extraction ability and robustness. Multiple constraints that incorporate the potential information of anatomical segmentation extracted by the discriminator can help the model adapt to different modal registration tasks. Through interpatient X-ray chest registration, the deep-supervised training method, and the proposed loss constraint are proved to improve the model's performance and training stability. The experimental results show that our model, compared with state-of-the-art methods, provides a more accurate spatial alignment relationship between different patients' lung organs while ensuring the displacement field's authenticity. Finally, we explored the relationship between the accuracy and validity of the model.
可变形医学图像配准具有理论研究和临床应用的必要价值。传统方法在配准精度和效率方面无法满足临床应用标准。本文提出了一种可变形生成对抗配准框架，该框架避免了对真实变形的依赖。所提出的基于嵌套U-Net的残差配准网络具有出色的特征提取能力和鲁棒性。结合鉴别器提取的解剖分割潜在信息的多个约束可以帮助模型适应不同模态的配准任务。通过患者间胸部X光配准，证明了深度监督训练方法和所提出的损失约束可提高模型的性能和训练稳定性。实验结果表明，与现有方法相比，我们的模型在确保位移场真实性的同时，为不同患者的肺器官提供了更准确的空间对齐关系。最后，我们探讨了模型准确性和有效性之间的关系。
