Abstract
Deformable medical image registration plays an important role in clinical diagnosis and treatment. Recently, the deep learning (DL) based image registration methods have been widely investigated and showed excellent performance in computational speed. However, these methods cannot provide enough registration accuracy because of insufficient ability in representing both the global and local features of the moving and fixed images. To address this issue, this paper has proposed the transformer based image registration method. This method uses the distinctive transformer to extract the global and local image features for generating the deformation fields, based on which the registered image is produced in an unsupervised way. Our method can improve the registration accuracy effectively by means of self-attention mechanism and bi-level information flow. Experimental results on such brain MR image datasets as LPBA40 and OASIS-1 demonstrate that compared with several traditional and DL based registration methods, our method provides higher registration accuracy in terms of dice values.
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Acknowledgment
This work was sponsored by the National Natural Science Foundation of China (Grant No. 61871440) and CAAI-Huawei MindSpore Open Fund. We gratefully acknowledge the support of MindSpore.
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Wang, Y., Qian, W., Li, M., Zhang, X. (2022). A Transformer-Based Network for Deformable Medical Image Registration. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13604. Springer, Cham. https://doi.org/10.1007/978-3-031-20497-5_41
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DOI: https://doi.org/10.1007/978-3-031-20497-5_41
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