Nonlinear Registration of Brain Magnetic Resonance Images with Cross Constraints of Intensity and Structure

Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes. However, these methods often lack constraint information and overlook semantic consistency, limiting their performance. To address these issues, we present a novel approach for medical image registration called the Dual-VoxelMorph, featuring a dual-channel cross-constraint network. This innovative network utilizes both intensity and segmentation images, which share identical semantic information and feature representations. Two encoder-decoder structures calculate deformation fields for intensity and segmentation images, as generated by the dual-channel cross-constraint network. This design facilitates bidirectional communication between grayscale and segmentation information, enabling the model to better learn the corresponding grayscale and segmentation details of the same anatomical structures. To ensure semantic and directional consistency, we introduce constraints and apply the cosine similarity function to enhance semantic consistency. Evaluation on four public datasets demonstrates superior performance compared to the baseline method, achieving Dice scores of 79.9%, 64.5%, 69.9%, and 63.5% for OASIS-1, OASIS-3, LPBA40, and ADNI, respectively.