Recent deep learning-based methods have shown promising results and runtime advantages in deformable image registration. However, analyzing the effects of hyperparameters and searching for optimal regularization parameters prove to be too prohibitive in deep learning-based methods. This is because it involves training a substantial number of separate models with distinct hyperparameter values. In this paper, we propose a conditional image registration method and a new self-supervised learning paradigm for deep deformable image registration. By learning the conditional features that correlated with the regularization hyperparameter, we demonstrate that optimal solutions with arbitrary hyperparameters can be captured by a single deep convolutional neural network. In addition, the smoothness of the resulting deformation field can be manipulated with arbitrary strength of smoothness regularization during inference. Extensive experiments on a large-scale brain MRI dataset show that our proposed method enables the precise control of the smoothness of the deformation field without sacrificing the runtime advantage or registration accuracy.
翻译:最近的深层学习方法在可变形图像登记方面显示了令人乐观的结果和时间优势。然而,分析超参数的效果和寻找最佳的正规化参数在深层学习方法中实在太过令人望而却步。这是因为它涉及培训大量不同的模型,具有不同的超参数值。在本文中,我们提出了一种有条件的图像登记方法和一种新的自我监督学习模式,用于深层可变形图像登记。通过了解与常态超参数相关的条件特征,我们证明一个单一的深层神经网络可以捕捉到任意超参数的最佳解决方案。此外,由此产生的变形场的顺利性可以被任意操纵,在推断过程中具有平稳性。大型大脑MRI数据集的广泛实验表明,我们拟议的方法能够精确控制变形场的平稳,而不会牺牲运行时间优势或登记准确性。