Abstract
Medical image registration is a crucial task for numerous medical applications, such as radiation therapy planning and surgical navigation. While deep learning methods have shown promise in improving registration accuracy, existing unsupervised registration methods based on convolutional neural networks struggle to capture long-range spatial relationships between voxels. Additionally, unsupervised registration methods based on Transformer are limited by their dependence on the induction bias of convolutional neural networks and the complexity of global attention. To address these limitations, we present LMConvMorph, a large kernel modern hierarchical convolutional model for unsupervised deformable medical image registration. LMConvMorph leverages larger receptive fields to identify spatial correspondence and employs a hierarchical design with smaller parameters to extract features at different scales, enabling effective feature extraction between moving and fixed images. Our approach yields significant improvements in registration performance. LMConvMorph is evaluated on a 3D human brain magnetic resonance image dataset, and the qualitative and quantitative results demonstrate its competitiveness with other baseline methods.
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Acknowledgements
This research was supported by Natural Science Foundation of Shandong province (Nos. ZR2019MF013, ZR2020KF015), Project of Jinan Scientific Research Leader’s Laboratory (No. 2018GXRC023).
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Liu, Z., Zhao, X., Niu, D., Yang, B., Zhang, C. (2023). LMConvMorph: Large Kernel Modern Hierarchical Convolutional Model for Unsupervised Medical Image Registration. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14090. Springer, Singapore. https://doi.org/10.1007/978-981-99-4761-4_19
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