Implicit Optimizer for Diffeomorphic Image Registration

• URL: http://arxiv.org/abs/2202.12498v1
• Date: Fri, 25 Feb 2022 05:04:29 GMT
• Title: Implicit Optimizer for Diffeomorphic Image Registration
• Authors: Kun Han, Shanlin Sun
• Abstract summary: We propose a rapid and accurate Implicit for Diffeomorphic Image Registration (IDIR) which utilizes the Deep Implicit Function as the neural velocity field. We evaluate our proposed 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.
• Score: 3.1970342304563037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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