提出新型3D医学图像分割网络，兼顾全局解剖结构与局部边界细节。
CDGC-Net: 3D Medical Image Segmentation with Cooperative Dual-Scale Self-Attention and Grouped Channel Modeling
- 双尺度注意力并行捕捉局部细节与长程上下文信息
- 分组通道建模增强跨通道依赖关系，提升特征表达能力
- 在多个数据集上精度领先，且模型更轻量高效
精准的3D医学图像分割需融合长程解剖上下文与精细边界细节。现有方法常将全局与局部特征分离建模，独立进行通道重校准，易导致语义不匹配、通道关系建模不足、空间-通道交互弱及冗余表示。本文提出CDGC-Net，结合协作式双尺度自注意力与分组层级通道建模。每个CDGC模块中，协作双尺度自注意力（CDSA）将注意力头分配至并行的局部窗口与全局稀疏分支，于同一特征层同时捕获精细空间细节与长程解剖上下文。两分支输出拼接为$N imes C$空间表征，直接输入分组层级通道注意力（GHCA）。GHCA将通道分为$r$组，建模组内与组间依赖关系。CDSA与GHCA共享键投影以保持特征参考一致性。残差特征对齐将优化后的特征与原始表征融合。在Synapse、ACDC、BraTS和LA数据集上，CDGC-Net分别取得86.96%、92.91%、82.56%和93.52%的平均Dice相似系数，优于次优报告值0.39、0.47、0.17和0.32个百分点。模型参数量25.83M，计算量28.62G FLOPs（输入尺寸$64\times128\times128$），相较UNETR++分别减少39.87%和40.30%，实现精度与复杂度的良好平衡。
原文摘要 · Abstract (English)
Accurate 3D medical image segmentation requires the integration of long-range anatomical context with fine boundary detail. Existing methods often model global and local features in separate modules or feature levels and perform channel recalibration independently. This may cause semantic mismatch between global context and local boundaries, insufficient channel relationship modeling, weak spatial-channel interaction, and redundant representations. We propose CDGC-Net, a 3D medical image segmentation network that combines cooperative dual-scale spatial attention with grouped hierarchical channel modeling. With-in each CDGC block, Cooperative Dual-Scale Self-Attention (CDSA) assigns attention heads to parallel local-window and global-sparse branches. The two branches capture fine spatial details and long-range anatomical context at the same feature level. Their outputs are concatenated into an $N\times C$ spatial representation and directly passed to Grouped Hierarchical Channel Attention (GHCA). GHCA organizes the channels into $r$ groups and models both within-group and cross-group dependencies. CDSA and GHCA reuse a shared key projection to maintain a consistent feature reference. Residual feature alignment subsequently integrates the refined features with the original representation. On the Synapse, ACDC, BraTS, and LA datasets, CDGC-Net achieved mean DSC values of 86.96\%, 92.91\%, 82.56\%, and 93.52\%, respectively, exceeding the next-highest reported values by 0.39, 0.47, 0.17, and 0.32 percentage points. CDGC-Net contains 25.83M parameters and 28.62G FLOPs for an input size of $64\times128\times128$, reducing these quantities by 39.87\% and 40.30\%, respectively, relative to UNETR++. These results indicate a favorable trade-off between segmentation accuracy and computational complexity.
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