School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China.
Key Lab of Intelligent Computing and Information Security in Universities of Shandong, Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Institute of Biomedical Sciences, Shandong Normal University, Jinan 250358, China.
J Healthc Eng. 2021 Apr 23;2021:5520196. doi: 10.1155/2021/5520196. eCollection 2021.
Image registration is a fundamental task in medical imaging analysis, which is commonly used during image-guided interventions and data fusion. In this paper, we present a deep learning architecture to symmetrically learn and predict the deformation field between a pair of images in an unsupervised fashion. To achieve this, we design a deep regression network to predict a deformation field that can be used to align the template-subject image pair. Specifically, instead of estimating the single deformation pathway to align the images, herein, we predict two halfway deformations, which can move the original template and subject into a pseudomean space simultaneously. Therefore, we train a symmetric registration network (S-Net) in this paper. By using a symmetric strategy, the registration can be more accurate and robust particularly on the images with large anatomical variations. Moreover, the smoothness of the deformation is also significantly improved. Experimental results have demonstrated that the trained model can directly predict the symmetric deformations on new image pairs from different databases, consistently producing accurate and robust registration results.
图像配准是医学图像分析中的一项基本任务，常用于图像引导干预和数据融合。在本文中，我们提出了一种深度学习架构，以非监督的方式对称地学习和预测一对图像之间的变形场。为了实现这一点，我们设计了一个深度回归网络来预测一个变形场，该变形场可用于对齐模板-对象图像对。具体来说，我们不是估计单个变形途径来对齐图像，而是预测两个中途变形，这可以同时将原始模板和对象移动到伪均值空间。因此，我们在本文中训练了一个对称注册网络（S-Net）。通过使用对称策略，注册可以更加准确和稳健，特别是在具有大的解剖变异的图像上。此外，变形的平滑度也得到了显著提高。实验结果表明，训练后的模型可以直接预测来自不同数据库的新图像对的对称变形，始终产生准确和稳健的配准结果。
