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Background <p>Gliomas are among the most common malignant brain tumors and exhibit substantial heterogeneity, complicating accurate detection and segmentation. Although multi-modal MRI is the clinical standard for glioma imaging, variability across modalities and high computational demands hamper effective automated segmentation.</p> Methods <p>We propose UKAN-EP, a novel 3D extension of the original 2D U-KAN model for multi-modal MRI brain tumor segmentation. While U-KAN integrates Kolmogorov-Arnold Network (KAN) layers into a U-Net backbone, UKAN-EP further incorporates Efficient Channel Attention (ECA) and Pyramid Feature Aggregation (PFA) modules to enhance inter-modality feature fusion and multi-scale feature representation. We also introduce a dynamic loss weighting strategy that adaptively balances cross-entropy and Dice losses during training.</p> Results <p>On the 2024 BraTS-GLI dataset, UKAN-EP achieves superior segmentation performance (e.g., Dice = 0.9001 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\pm\)</EquationSource> </InlineEquation> 0.0127 and IoU = 0.8257 <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\pm\)</EquationSource> </InlineEquation> 0.0186 for the whole tumor) while requiring substantially fewer computational resources (223.57 GFLOPs and 11.30M parameters) compared to strong baselines including U-Net, Attention U-Net, Swin UNETR, VT-Unet, TransBTS, and 3D U-KAN. An extensive ablation study further confirms the effectiveness of ECA and PFA and shows the limited utility of self-attention and spatial attention alternatives.</p> Conclusion <p>UKAN-EP demonstrates that combining the expressive power of KAN layers with lightweight channel-wise attention and multi-scale feature aggregation improves the accuracy and efficiency of brain tumor segmentation.</p>

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UKAN-EP: enhancing U-KAN with efficient attention and pyramid aggregation for 3D multi-modal MRI brain tumor segmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月22日

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  • Yanbing Chen,
  • Tianze Tang,
  • Taehyo Kim,
  • Hai Shu

摘要

Background

Gliomas are among the most common malignant brain tumors and exhibit substantial heterogeneity, complicating accurate detection and segmentation. Although multi-modal MRI is the clinical standard for glioma imaging, variability across modalities and high computational demands hamper effective automated segmentation.

Methods

We propose UKAN-EP, a novel 3D extension of the original 2D U-KAN model for multi-modal MRI brain tumor segmentation. While U-KAN integrates Kolmogorov-Arnold Network (KAN) layers into a U-Net backbone, UKAN-EP further incorporates Efficient Channel Attention (ECA) and Pyramid Feature Aggregation (PFA) modules to enhance inter-modality feature fusion and multi-scale feature representation. We also introduce a dynamic loss weighting strategy that adaptively balances cross-entropy and Dice losses during training.

Results

On the 2024 BraTS-GLI dataset, UKAN-EP achieves superior segmentation performance (e.g., Dice = 0.9001 \(\pm\) 0.0127 and IoU = 0.8257 \(\pm\) 0.0186 for the whole tumor) while requiring substantially fewer computational resources (223.57 GFLOPs and 11.30M parameters) compared to strong baselines including U-Net, Attention U-Net, Swin UNETR, VT-Unet, TransBTS, and 3D U-KAN. An extensive ablation study further confirms the effectiveness of ECA and PFA and shows the limited utility of self-attention and spatial attention alternatives.

Conclusion

UKAN-EP demonstrates that combining the expressive power of KAN layers with lightweight channel-wise attention and multi-scale feature aggregation improves the accuracy and efficiency of brain tumor segmentation.