Han Zhou1,2, Hongtao Xu1,2, Xinyue Chang1,2, Wei Zhang1,2, Heng Dong1,2,*
CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2295-2313, 2024, DOI:10.32604/cmc.2024.047754
Abstract 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 More >
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