A Dual Domain Collaborative Network for Polyp Segmentation

Accurate polyp segmentation in colonoscopy images is essential for early colorectal cancer detection but remains a challenging problem due to the limitations in existing methods for optimizing boundary features and aligning cross-level representations. Specifically, the indistinct polyp boundaries and scale variations across different feature levels pose significant challenges for segmentation accuracy. To address these issues, we propose a dual domain collaborative network (DDCNet) that introduces two novel modules: a frequency context enhancement module (FCEM), which operates in the frequency domain to refine high- and low-frequency features, and a cross-level shift-recalibrated fusion module (CSFM), which improves multi-scale feature alignment in the spatial domain. The FCEM improves boundary precision by adaptively refining high-frequency boundary features and enhancing low-frequency contextual information, while the CSFM mitigates cross-level feature misalignment by dynamically recalibrating multi-scale features throughout the encoder-decoder architecture. Additionally, we design a hybrid loss function that integrates boundary, cross-entropy, and frequency consistency losses to further boost segmentation performance. Experimental results on three benchmark datasets (Kvasir-SEG, CVC-ClinicDB, and CVC-ColonDB) demonstrate that DDCNet achieves state-of-the-art performance, with Dice coefficients of 0.9343, 0.9447, and 0.8155, respectively. These results represent improvements of 1.0%-1.5% over the best existing methods. Ablation studies further validate the individual contributions of FCEM, CSFM, and the hybrid loss function. Additionally, we compared the proposed loss function with three commonly used functions.