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.

翻译：背景：胶质瘤是最常见的恶性脑肿瘤之一，具有显著的异质性，使得精确检测与分割变得复杂。尽管多模态MRI是胶质瘤成像的临床标准，但模态间的差异性和高计算需求阻碍了有效的自动分割。方法：我们提出UKAN-EP，一种用于多模态MRI脑肿瘤分割的原始2D U-KAN模型的新型3D扩展。U-KAN将Kolmogorov-Arnold Network（KAN）层集成到U-Net骨干网络中，而UKAN-EP进一步引入了高效通道注意力（ECA）模块和金字塔特征聚合（PFA）模块，以增强模态间特征融合与多尺度特征表示。我们还提出了一种动态损失加权策略，在训练过程中自适应地平衡交叉熵损失与Dice损失。结果：在2024年BraTS-GLI数据集上，UKAN-EP实现了卓越的分割性能（例如，全肿瘤区域的Dice = 0.9001 $\pm$ 0.0127，IoU = 0.8257 $\pm$ 0.0186），同时与包括U-Net、Attention U-Net、Swin UNETR、VT-Unet、TransBTS和3D U-KAN在内的强基线模型相比，所需计算资源显著减少（223.57 GFLOPs和11.30M参数）。一项全面的消融研究进一步证实了ECA和PFA的有效性，并表明自注意力与空间注意力替代方案的效用有限。结论：UKAN-EP证明，将KAN层的强大表达能力与轻量级通道注意力及多尺度特征聚合相结合，能够提高脑肿瘤分割的准确性与效率。