Deformable Image Registration using Neural ODEs

• URL: http://arxiv.org/abs/2108.03443v1
• Date: Sat, 7 Aug 2021 12:54:17 GMT
• Title: Deformable Image Registration using Neural ODEs
• Authors: Yifan Wu, Tom Z.Jiahao, Jiancong Wang, Paul A.Yushkevich, James C.Gee, M.Ani Hsieh
• Abstract summary: We present a generic, fast, and accurate diffeomorphic image registration framework that leverages neural ordinary differential equations (NODEs) Compared with traditional optimization-based methods, our framework reduces the running time from tens of minutes to tens of seconds. Our experiments show that the registration results of our method outperform state-of-the-arts under various metrics.
• Score: 15.245085400790002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deformable image registration, aiming to find spatial correspondence between a given image pair, is one of the most critical problems in the domain of medical image analysis. In this paper, we present a generic, fast, and accurate diffeomorphic image registration framework that leverages 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. Compared with traditional optimization-based methods, our framework reduces the running time from tens of minutes to tens of seconds. Compared with recent data-driven deep learning methods, our framework is more accessible since it does not require large amounts of training data. Our experiments show that the registration results of our method outperform state-of-the-arts under various metrics, indicating that our modeling approach is well fitted for the task of deformable image registration.

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