提出解耦连接特征的网络，提升医学图像分割精度与边界平滑性。
DCFFSNet: Deep Connectivity Feature Fusion Separation Network for Medical Image Segmentation
- 通过解耦策略量化连接特征与其他特征的强度差异
- 在三个数据集上均超越现有模型，最高提升1.3% Dice
- 适合需要高精度边缘分割的临床医学图像分析场景
医学图像分割利用拓扑连通性理论提升边缘精度和区域一致性。然而，现有深度网络在引入连通性时常强制添加额外特征模块，导致特征空间耦合且缺乏标准化机制来量化不同特征强度。为此，我们提出DCFFSNet（双连通性特征融合-分离网络），引入创新的特征空间解耦策略，量化连通性特征与其他特征的相对强度，并构建深层连通性特征融合-分离架构，动态平衡多尺度特征表达。在ISIC2018、DSB2018和MoNuSeg数据集上进行实验：在ISIC2018上，相比最优模型CMUNet，Dice提升1.3%，IoU提升1.2%；在DSB2018上，超越TransUNet，Dice提升0.7%，IoU提升0.9%；在MoNuSeg上，超过CSCAUNet，Dice提升0.8%，IoU提升0.9%。结果表明，DCFFSNet在所有指标上均优于主流方法，有效缓解分割碎片化问题，实现平滑边缘过渡，显著提升临床可用性。
原文摘要 · Abstract (English)
Medical image segmentation leverages topological connectivity theory to enhance edge precision and regional consistency. However, existing deep networks integrating connectivity often forcibly inject it as an additional feature module, resulting in coupled feature spaces with no standardized mechanism to quantify different feature strengths. To address these issues, we propose DCFFSNet (Dual-Connectivity Feature Fusion-Separation Network). It introduces an innovative feature space decoupling strategy. This strategy quantifies the relative strength between connectivity features and other features. It then builds a deep connectivity feature fusion-separation architecture. This architecture dynamically balances multi-scale feature expression. Experiments were conducted on the ISIC2018, DSB2018, and MoNuSeg datasets. On ISIC2018, DCFFSNet outperformed the next best model (CMUNet) by 1.3% (Dice) and 1.2% (IoU). On DSB2018, it surpassed TransUNet by 0.7% (Dice) and 0.9% (IoU). On MoNuSeg, it exceeded CSCAUNet by 0.8% (Dice) and 0.9% (IoU). The results demonstrate that DCFFSNet exceeds existing mainstream methods across all metrics. It effectively resolves segmentation fragmentation and achieves smooth edge transitions. This significantly enhances clinical usability.
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