Abstract
In this paper, a novel unsupervised network for medical image registration called VAN (Voting and Attention based Network) is proposed, in which the final deformation field is determined by the voting process between multiple registration branches. To reduce model parameters, multiple registration branches share one encoder. Besides, the attention mechanism is introduced, which further improves the network accuracy. We also adopt the method of single training and multiple-registrations to deal with the problem of the large deformation field. The experimental results show that the registration effect of our proposed network outperforms the baselines VoxelMorph and Symmetric Normalization (SyN) on three brain MRI image datasets.
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Acknowledgment
This work was supported by Shanghai Science and Technology Innovation Action Plan (18441909000, 20511100200), Science and Technology Commission of Shanghai Municipality (14DZ2260800), and OSTF foundation. The authors would like to thank Prof. Meng Yao of East China Normal University for fruitful discussion.
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Zu, Z., Zhang, G., Peng, Y., Ye, Z., Shen, C. (2021). VAN: Voting and Attention Based Network for Unsupervised Medical Image Registration. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13031. Springer, Cham. https://doi.org/10.1007/978-3-030-89188-6_29
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DOI: https://doi.org/10.1007/978-3-030-89188-6_29
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