Diffeomorphic Image Registration Using Lipschitz Continuous Residual Networks
Diffeomorphic Image Registration Using Lipschitz Continuous Residual Networks
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发表时间：
2022
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通讯作者：
Ankita Joshi;Yi Hong
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作者：
Ankita Joshi;Yi Hong
图像配准是医学图像分析中的一项重要任务。我们提出了两种新的无监督微分同胚图像配准网络，该网络使用深度残差网络(ResNets)作为基本的由常微分方程组(ODES)控制的连续微分同胚集的数值逼近，被视为欧拉离散化方案。在考虑基于常微分方程组的微分同胚的参数化时，我们同时考虑了平稳和非平稳(时变)速度场作为驱动速度来求解常微分方程组，这导致了我们提出的两种用于微分同胚配准的结构。我们还利用两种结构的残差网络上的Lipschitz连续性将速度场的允许Hilbert空间定义为再生核Hilbert空间(RKHS)，并使速度场的光滑性正则化。我们应用这两个配准网络来对齐和分割OASIS脑MRI数据集。实验结果表明，我们的模型具有较高的计算效率，并且获得了与更平滑的变形场相当的配准结果。
Image registration is an essential task in medical image analysis. We propose two novel unsupervised diffeomorphic image registration networks, which use deep Residual Networks (ResNets) as numerical approximations of the underlying continuous diffeomorphic setting governed by ordinary differential equations (ODEs), viewed as a Eulerian discretization scheme. While considering the ODE-based parameterizations of diffeomorphisms, we con-sider both stationary and non-stationary (time varying) velocity fields as the driving velocities to solve the ODEs, which give rise to our two proposed architectures for diffeomorphic registration. We also employ Lipschitz-continuity on the Residual Networks in both architectures to define the admissible Hilbert space of velocity fields as a Reproducing Kernel Hilbert Spaces (RKHS) and regularize the smoothness of the velocity fields. We apply both registration networks to align and segment the OASIS brain MRI dataset. Experimental results demonstrate that our models are computational efficient and achieve comparable registration results with a smoother deformation field.