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We propose a fluid-based registration framework of medical images based onimplicit neural representation. By integrating implicit neural representationand Large Deformable Diffeomorphic Metric Mapping (LDDMM), we employ aMultilayer Perceptron (MLP) as a velocity generator while optimizing velocityand image similarity. Moreover, we adopt a coarse-to-fine approach to addressthe challenge of deformable-based registration methods dropping into localoptimal solutions, thus aiding the management of significant deformations inmedical image registration. Our algorithm has been validated on a pairedCT-CBCT dataset of 50 patients,taking the Dice coefficient of transferredannotations as an evaluation metric. Compared to existing methods, our approachachieves the state-of-the-art performance.

INR-LDDMM: Fluid-based Medical Image Registration Integrating Implicit Neural Representation and Large Deformation Diffeomorphic Metric Mapping