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- W4386080661 abstract "We propose a flow-based registration framework of medical images based on implicit neural representation. By integrating implicit neural representation and Large Deformable Diffeomorphic Metric Mapping (LDDMM), we employ a Multilayer Perceptron (MLP) as a velocity generator while optimizing velocity and image similarity. Moreover, we adopt a coarse-to-fine approach to address the challenge of deformable-based registration methods dropping into local optimal solutions, thus aiding the management of significant deformations in medical image registration. Our algorithm has been validated on a paired CT-CBCT dataset of 50 patients,taking the dice coefficient of transferred annotations as an evaluation metric. Compared to existing methods, our approach achieves the state-of-the-art performance." @default.
- W4386080661 created "2023-08-23" @default.
- W4386080661 creator A5056242195 @default.
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- W4386080661 date "2023-08-18" @default.
- W4386080661 modified "2023-09-25" @default.
- W4386080661 title "INR-LDDMM: Fluid-based Medical Image Registration Integrating Implicit Neural Representation and Large Deformation Diffeomorphic Metric Mapping" @default.
- W4386080661 doi "https://doi.org/10.48550/arxiv.2308.09473" @default.
- W4386080661 hasPublicationYear "2023" @default.
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