## 1 Introduction  
Colorectal cancer (CRC) is considered as one of the three major malignant tumors in the world [1]. It is estimated that there are more than 1.85 million cases and 850,000 deaths every year in the world, and especially in low-income areas, the incidence rate and mortality rate have increased significantly [2]. Early detection and precise segmentation of colonic mucosal polyps are crucial for reducing CRC-related deaths. Optical colonoscopy (OC) is a gold standard screening technique that can locate and characterize colorectal polyps, promoting their removal before malignant transformation. However, the accurate identification and segmentation of polyps during colonoscopy remain clinically demanding procedures. Significant variations in polyp morphology (size, color, texture) combined with low contrast against surrounding mucosa substantially increase the risks of both missed detection and false-positive diagnoses. To address these issues, numerous scholars have conducted research on colorectal polyp segmentation algorithms. Colorectal polyp segmentation methods can be broadly categorized into traditional methods relying on manual feature extraction and deep learning-based methods. Traditional methods involve extracting low-level features such as color, contour, edge, and texture, and then using classifiers to distinguish between polyps and normal colorectal mucosa. However, these methods heavily rely on the expertise of the designer, and due to the differences in size, shape, and texture of different polyps, they often perform poorly, leading to issues such as missed detection and false positives. In recent years, with the development of deep learning technology, Convolutional Neural Networks (CNNs) have been widely applied in the field of medical image segmentation. For instance, a symmetric U-shaped network [3] capable of integrating multi-scale features was proposed to achieve precise pixel-level segmentation through its unique encoder-decoder architecture. The Attention U-Net [4] architecture introduced attention gates to enhance accuracy by focusing on regions of interest, effectively allocating computational resources to diagnostically significant areas. ABC-Net [5] introduced a dual-branch architecture with a shared encoder and mutually constrained decoders for simultaneous segmentation of colorectal polyp regions and their anatomical boundaries. SegT [6] improved the boundary between benign regions and polyps by designing a separate edge guidance module, including a separator and an edge guidance block. PraNet [7] utilized Parallel Partial Decoders (PPDs) to aggregate high-level features and established relationships between target regions and boundaries through an inverse attention module. As another representative of deep learning, the Transformer has been applied by numerous scholars in fields such as medical image segmentation due to its outstanding performance in the field of natural language processing. For example, TransUNet [8] combined the Transformer as an encoder with a CNN decoder to address the ambiguous boundary between pancreatic tissue and surrounding organs through global context modeling. PMTrans [9] utilized a pyramid-shaped Transformer branch to fuse with CNN features, effectively capturing multi-scale morphological features of retinal blood vessels. Med-Former [10] employed a hierarchical Transformer architecture to process 3D cardiac MRI data, modeling the spatial topological relationships between ventricles and atria through a self-attention mechanism. Despite various advancements made by existing segmentation models in polyp segmentation, with some algorithms achieving relatively accurate segmentation results, the problem of polyp segmentation still poses many challenges:  
* Noise Interference: when the polyp image is disturbed by noise such as bubbles, it is easy to cause discontinuity and blurred segmentation edges.