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ASF-LKUNet: Adjacent-Scale Fusion U-Net with Large-kernel for Medical Image Segmentation
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  • Rongfang Wang ,
  • zhaoshan Mu ,
  • kai wang ,
  • Hui Liu ,
  • Zhiguo Zhou ,
  • Shuiping Gou ,
  • Jing Wang ,
  • Licheng Jiao
Rongfang Wang
Xidian University

Corresponding Author:[email protected]

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zhaoshan Mu
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Zhiguo Zhou
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Shuiping Gou
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Jing Wang
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Licheng Jiao
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Abstract

In this paper, we propose an adjacent-scale fusion 2.5D U-Net with large-kernel (ASF-LKUNet) for multi-class medical image segmentation tasks. To reduce model complexity, we utilize a u-shaped encoder-decoder as the base architecture of ASF-LKUNet. In the encoder path, we design the large-kernel residual block, which combines the large and small kernels and can simultaneously capture the global and local features while retaining the advantages of ViT. Furthermore, we develop an adjacent-scale GRN channel attention mechanism that incorporates the low-level details with the high-level semantics by fusing the feature of adjacent scales. The adaptive fusion is implemented by the improved large-kernel channel attention based on global response normalization (GRN). In ASF-LKUNet, all the large-kernel apply depth-wise convolutions to further reduce the complexity. Our proposed method is compared with ten other methods, including those based on UNets, multi-scale fusion, 3D CNN, and ViTs. Extensive experiments of performance and interpretability analysis show that ASF-LKUNet outperforms various competing methods with less model complexity on different medical applications, including multi-organ segmentation in CT images and cardiac multi-structure segmentation in MRI images.