ALU-Net: an attention-based mechanism lightweight U-Net for multimodal brain tumor segmentation

Guocai Huang; Zhenping Chen; Chao Yang; Tao Yu

doi:10.1117/12.3026721

27 March 2024 ALU-Net: an attention-based mechanism lightweight U-Net for multimodal brain tumor segmentation

Guocai Huang, Zhenping Chen, Chao Yang, Tao Yu

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131050M (2024) https://doi.org/10.1117/12.3026721
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

This paper designs a more efficient CNN (Convolutional Neural Network) architecture to extract more spatial features to solve this problem. A Double Channel Convolution(DCC) is designed using two multiple convolution modules to capture more spatial features and a residual module to prevent network performance degradation. The skip connection employs spatial and channel attention mechanisms for dual capturing of associations among global and local features between space and channel to enhance the correlation of features from modalities to modalities as well as the correlation judgment of Region of Interests (ROIs) and boundary information. The effectiveness of the network is verified by the experimental results with open access available dataset BraTS21, which shows that the Dice similarity coefficient (DSC) in the segmented brain tumors in the Enhance Tumor(ET),Tumor Core(TC) and the Whole Tumor(WT) were 0.832, 0.873 and 0.915, which are 1.2%, 1.16% and 2.2% higher than the DSC, JSC and Sensitivity of the U-Net model, respectively. The model size is reduced by 7.6 M. The experimental results show that the ALU-Net model proposed in this paper can achieve better performance and more computational efficient. The model showed good performance in automatic brain tumor segmentation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guocai Huang, Zhenping Chen, Chao Yang, and Tao Yu "ALU-Net: an attention-based mechanism lightweight U-Net for multimodal brain tumor segmentation", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131050M (27 March 2024); https://doi.org/10.1117/12.3026721

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KEYWORDS

Image segmentation

Tumors

Brain

Convolution

Data modeling

Semantics

Magnetic resonance imaging

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