PSFS-Net: Dynamic frequency-spatial synergistic perception network for polyp segmentation via hierarchical context refinement and frequency-domain decoupling

Abstract

Accurate polyp segmentation from colonoscopy images is pivotal for the early detection of colorectal cancer (CRC), significantly enhancing diagnostic efficiency and reliability in clinical practice. While recent methods have achieved notable progress, they often suffer from two critical limitations: (1) inadequate frequency and spatial feature representation, as most approaches remain biased toward spatial-domain learning and, even when incorporating frequency information, tend to overlook the hierarchical variability of frequency distributions across feature levels, resulting in suboptimal utilization of frequency cues; and (2) insufficient cross-level feature integration, limiting the ability to effectively capture both global semantics and fine-grained boundary details. To address these issues, we propose PSFS-Net, a novel dynamic frequency-spatial synergistic polyp segmentation framework that jointly leverages spatial and frequency domain information for hierarchical context refinement and cross-level fusion, which mainly includes Frequency-aware Cross-scale Fusion Module (FACFM), Dual-stream Global–Local Interaction Module (DGIM), and Dual Attention Cross-modulation Module (DCM). Specifically, FACFM is designed to extract frequency domain cues and adaptively decoupling high/low-frequency components from full-spectrum information by employs Discrete Fourier Transform and an adaptive Dynamic Gaussian Filters. DGIM is introduced to enable mutual refinement between high-level semantic representations and low-level spatial details through dedicated global and local processing branches. DCM is presented to further aggregate global contexts with local details via dual-attention mechanisms, alleviating semantic gaps. Extensive evaluations on five public polyp segmentation datasets demonstrate that PSFS-Net delivers competitive and excellent performances. Our code is available at https://github.com/chljzh25/PSFS-Net.