DCFFSNet: Deep Connectivity Feature Fusion Separation Network for Medical Image Segmentation
This work improves medical image segmentation for clinical applications by reducing fragmentation and enhancing edge transitions, though it appears incremental as it builds on existing connectivity integration methods.
The paper tackles the problem of medical image segmentation by addressing coupled feature spaces in existing deep networks that integrate connectivity, proposing DCFFSNet which introduces a feature space decoupling strategy to quantify feature strengths and dynamically balance multi-scale features. The results show that DCFFSNet outperforms existing models on three datasets, with improvements such as 1.3% Dice and 1.2% IoU on ISIC2018.
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.