结肠镜图像中结直肠息肉的准确分割对于结直肠癌（CRC）的有效诊断和治疗至关重要。然而，当前基于深度学习的方法主要依赖于跨多个尺度的RGB信息融合，由于RGB域信息有限以及多尺度聚合过程中特征错位的挑战，导致准确识别息肉受到限制。为了解决这些限制，我们提出了带有分流变压器的息肉分割网络（PSTNet），这是一种集成图像中存在的 RGB 和频域线索的新颖方法。 PSTNet 包含三个关键模块：用于提取频率线索和捕获息肉特征的频率表征注意模块（FCAM）、用于对齐语义信息和减少未对齐噪声的特征补充对齐模块（FSAM）以及用于将频率线索与高级语义相结合以实现有效的息肉分割。对具有挑战性的数据集进行的大量实验表明，PSTNet 在各种指标上的息肉分割准确性方面都有显着提高，始终优于最先进的方法。频域线索的集成和 PSTNet 的新颖架构设计有助于推进计算机辅助息肉分割，促进更准确的 CRC 诊断和管理。我们的源代码可供参考：https://github.com/clearxu/PSTNet。

Accurate segmentation of colorectal polyps in colonoscopy images is crucial for effective diagnosis and management of colorectal cancer (CRC). However, current deep learning-based methods primarily rely on fusing RGB information across multiple scales, leading to limitations in accurately identifying polyps due to restricted RGB domain information and challenges in feature misalignment during multi-scale aggregation. To address these limitations, we propose the Polyp Segmentation Network with Shunted Transformer (PSTNet), a novel approach that integrates both RGB and frequency domain cues present in the images. PSTNet comprises three key modules: the Frequency Characterization Attention Module (FCAM) for extracting frequency cues and capturing polyp characteristics, the Feature Supplementary Alignment Module (FSAM) for aligning semantic information and reducing misalignment noise, and the Cross Perception localization Module (CPM) for synergizing frequency cues with high-level semantics to achieve efficient polyp segmentation. Extensive experiments on challenging datasets demonstrate PSTNet's significant improvement in polyp segmentation accuracy across various metrics, consistently outperforming state-of-the-art methods. The integration of frequency domain cues and the novel architectural design of PSTNet contribute to advancing computer-assisted polyp segmentation, facilitating more accurate diagnosis and management of CRC. Our source code is available for reference at https://github.com/clearxu/PSTNet.