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摘要
The UNet network can be used to predict the dense displacement field in the full-resolution spatial domain, and has achieved great success in the field of medical image registration. However, for three-dimensional images with large deformation, there are still shortcomings such as long running time, inability to effectively maintain the topological structure, and easily leading to the loss of spatial features. A large kernel multi-scale deformable medical image registration network based on cross-attention (LK-CAUNet) is proposed. Based on the classical UNet network, the cross-attention module is introduced to achieve efficient and multi-level semantic feature fusion. The large kernel asymmetric parallel convolution is equipped. It has the ability to learn multi-scale features and complex structures. Besides, an additional square and scaling module is added to let it have the advantages of topological conservation and transform reversibility. Using the brain MRI dataset, it is demonstrated that the proposed method has significantly improved the registration performance compared with the eighteen classical registration methods. Especially compared with the most advanced TransMorph registration method, the Dice score can be improved by 8%, and the parameter quantity is only one fifth of it. ? 2023 Zhejiang University Press.
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