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DW-SCA Unet: medical image segmentation based on depth-wise separable convolutional attention U-shaped network

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Abstract

Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning. Recently, Transformer has achieved great success in various medical image segmentation tasks, which has long-range dependence compared to convolution neural network (CNN). However, self-attention in Transformer regards 2D images as 1D sequences, which destroys the crucial 2D structure of the image, which only considers the adaptability in spatial dimension but ignores the adaptability in channel dimension. Transformer can result in limited localization abilities due to insufficient low-level details. To solve these problems, this paper proposes Depth-wise Separable Convolutional Attention U-shape network (DW-SCA Unet), a U-shaped encoding-decoding network for medical image segmentation. The depth-wise separable convolutional attention block absorbs the advantages of depth-wise separable convolution and transformer, including long-range dependence, local structural information and adaptability. Specifically, we use a depth-wise separable convolutional attention block as the encoder to extract contextual features. And a symmetric depth-wise separable convolutional attention block with patch expanding layer is designed to perform up-sampling operation to restore the spatial resolution of the feature maps. On the skip connections of U-shaped networks, we use Channel-wise Cross-Attention (CCA) to guide the fused multi-scale channel-wise information to efficiently connect to decoder features for eliminating the ambiguity. Experimental results showed that DW-SCA Unet achieved better performance in the Synapse multi-organ segmentation task with segmentation accuracies of 80.54%% (DSC) and 20.02% (HD). The experiments on multi-organ and cardiac segmentation tasks also demonstrate the superiority, effectiveness and robustness of our DW-SCA Unet.

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Data availability

The Synapse datasets that support the findings of this study are available in [repository name “Synapse Storage” at https://doi.org/10.7303/syn3193805[doi], Synapse ID [syn3193805]. The ACDC datasets analysed during the current study are available in the “Automated Cardiac Diagnosis Challenge (ACDC)” repository. The ACDC dataset can be accessed on the Internet (https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html).

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61602528).

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  1. College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China

    Yi Zhou, Wei Tian, Yichi Zhang & Chuzheng Wang

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  1. Yi Zhou
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  2. Wei Tian
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Correspondence to Chuzheng Wang.

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Zhou, Y., Tian, W., Zhang, Y. et al. DW-SCA Unet: medical image segmentation based on depth-wise separable convolutional attention U-shaped network. Multimed Tools Appl 83, 8893–8910 (2024). https://doi.org/10.1007/s11042-023-15960-3

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