Implicit Optimizer for Diffeomorphic Image Registration
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Diffeomorphic image registration is the underlying technology in medical image processing which enables the invertibility and point-to-point correspondence. Recently, numerous learning-based methods utilizing convolutional neural networks (CNNs) have been proposed for registration problems. Compared with the speed boosting, accuracy improvement brought by the complicated CNN-based methods is minor. To tackle this problem, we propose a rapid and accurate Implicit Optimizer for Diffeomorphic Image Registration (IDIR) which utilizes the Deep Implicit Function as the neural velocity field (NVF) whose input is the point coordinate p and output is velocity vector at that point v. To reduce the huge memory consumption brought by NVF for 3D volumes, a sparse sampling is employed to the framework. We evaluate our method on two 3D large-scale MR brain scan datasets, the results show that our proposed method provides faster and better registration results than conventional image registration approaches and outperforms the learning-based methods by a significant margin while maintaining the desired diffeomorphic properties.
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