Deformable image registration (DIR), aiming to find spatial correspondence between images, is one of the most critical problems in the domain of medical image analysis. In this paper, we present a novel, generic, and accurate diffeomorphic image registration framework that utilizes neural ordinary differential equations (NODEs). We model each voxel as a moving particle and consider the set of all voxels in a 3D image as a high-dimensional dynamical system whose trajectory determines the targeted deformation field. Our method leverages deep neural networks for their expressive power in modeling dynamical systems, and simultaneously optimizes for a dynamical system between the image pairs and the corresponding transformation. Our formulation allows various constraints to be imposed along the transformation to maintain desired regularities. Our experiment results show that our method outperforms the benchmarks under various metrics. Additionally, we demonstrate the feasibility to expand our framework to register multiple image sets using a unified form of transformation,which could possibly serve a wider range of applications.
翻译:旨在寻找图像间空间对应的变形图像登记(DIR)是医学图像分析领域最关键的问题之一。在本文中,我们提出了一个新颖的、通用的和准确的二变形图像登记框架,使用神经普通差异方程式(NODEs ) 。我们将每个 voxel 模型作为移动粒子,并将3D 图像中的所有 voxel 组合视为一个高维动态系统,其轨迹决定了目标变形场。我们的方法利用深神经网络在建模动态系统时的表达力,同时优化图像对子和相应变换之间的动态系统。我们的配方允许在转换过程中施加各种限制,以保持理想的规律性。我们的实验结果表明,我们的方法超过了各种计量标准下的基准。此外,我们展示了扩大框架的可行性,以便使用统一的变换形式来登记多个图像集,这可能有助于更广泛的应用。