Purpose

Imaging registration have a significant contribution to guide and support physicians in the process of decision making for diagnosis, prognosis and treatment. However, existing registration methods based on convolutional neural network can not extract global feature effectively, which significantly influences the registration performance. Moreover, the smoothness of displacement vector field (DVF) fails to be ensured due to miss folding penalty.

Methods

In order to capture abundant global information as well as local information, we have proposed a novel 3D deformable image registration network based on Transformer (TransDIR). In the encoding phase, Transformer with the atrous reduction attention block is designed to capture the long-distance dependencies which are crucial for extracting global information. Zero padding position encoder is embedded into Transformer to capture the local information. In the decoding phase, up-sampling module based on attention mechanism is designed to increase the significance of ROIs. Because of adding folding penalty term into loss function, the smoothness of DVF is improved.

Results

Finally, we carried out experiments on OASIS, LPBA40, MGH10 and MM-WHS open datasets to validate the effectiveness of TransDIR. Compared with LapIRN, the DSC score is improved by 1.1% and 0.9% on OASIS and LPBA40, separately. In addition, compared with VoxelMorph, the DSC score is improved by 2.8% on the basis of folding index decreased by hundreds of times on MM-WHS.

Conclusions

The results show that the TransDIR achieves robust registration and promising generalizability compared with LapIRN and VoxelMorph.

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