DCFFSNet: Deep Connectivity Feature Fusion Separation Network for Medical Image Segmentation

TL;DR

DCFFSNet introduces a novel feature decoupling and fusion architecture that improves medical image segmentation accuracy and edge quality by effectively balancing connectivity features with other features across multiple datasets.

Contribution

The paper proposes a new deep network with a feature space decoupling strategy for better connectivity feature management in segmentation tasks.

Findings

Outperforms existing models on multiple datasets.

Achieves higher Dice and IoU scores.

Enhances edge smoothness and segmentation coherence.

Abstract

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.