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A light-weight rectangular decomposition large kernel convolution network for deformable medical image registration
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DOI：10.1016/j.bspc.2024.106476.png)
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Abstract
En 中文
The performance and speed of medical image registration have been greatly boosted by advanced deeplearning based methods. However, most current methods are challenged by large deformations between input images, which necessitate a compromise in computational cost to enhance the model's receptive field and its ability to model long-range spatial relationships for improving registration performance. In order to enhance the performance of registration for images with large deformations at a lower computational cost, in this paper, we propose a light -weight registration model with the ability to model large receptive fields and long-range spatial relationships, named LL -Net. The core components of LL -Net consist of a Rectangular Decomposition Large Kernel Attention (RD-LKA) layer and a Spatial and Channel Fusion Attention (SC -Fusion) layer. The RD-LKA layer utilizes anisotropic depth -wise large kernel convolutions to capture large receptive fields with an extremely low parameter count while modeling long-range spatial relationships. Moreover, the SC -Fusion layer enhances the model's feature fusion capability and strengthens feature representations at critical locations. Our LL -Net exhibits state-of-the-art performance across multiple datasets. Specifically, it achieves a Dice score of 76.7% and an HD95 of 2.983 mm on the IXI dataset, and a Dice score of 87.8% and an HD95 of 1.042 mm on the OASIS dataset. Experimental results substantiate the efficacy of LL -Net in capturing large receptive fields and modeling long-range spatial relationships. The code for LL -Net is available at https://github.com/BoyOfChu/LL_Net.
Keywords:
Deformable registration
Light-weight network
Large receptive field
Anisotropic rectangular decomposition
Long-range spatial relationships
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