# MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
## 1 Introduction  
Colorectal cancer (CRC) ranks as the third most prevalent and serious cancer worldwide [1] and is characterized by a high incidence and mortality rate. A strong correlation exists between polyps--especially adenomatous polyps--and the development of CRC, as these polyps represent a critical precursor stage. Consequently, effective prevention of CRC necessitates early detection through screening tests such as colonoscopy. Colonoscopy remains the gold standard for CRC screening due to its high diagnostic accuracy; however, it is heavily dependent on the operator's expertise and the procedural environment. Despite advancements, challenges such as the complex intestinal topology, inconsistent lighting conditions, and continuous organ deformation contribute to a substantial polyp miss rate. Studies reveal a missed detection rate of 26.8% for polyps in the right colon and 21.4% for polyps in the left colon [2, 3]. These limitations highlight the urgent need for automated polyp segmentation systems to assist clinicians in improving diagnostic reliability and reducing error rates. Deep learning has emerged as a transformative approach in medical image analysis, offering robust solutions for developing next-generation imaging applications [4]. Theintroduction of U-Net by [PERSON] et al. in 2015 [PERSON] et al. (2015) was a significant milestone, establishing deep learning as a cornerstone in medical image segmentation. Nevertheless, polyp segmentation presents unique challenges, including an imbalance between foreground and background classes, as well as blurred and indistinct edges. Previously, a potential solution came from the statistical region-based segmentation method developed by [PERSON] et al., which improves segmentation by modeling image regions statistically, particularly for ultrasound images with complex backgrounds [PERSON] et al. (2015). While this method was not initially designed for polyp segmentation, its approach to handling low-contrast regions can be beneficial in our context as well. Polyp boundaries, influenced by variations in shape, size, light, and texture, can make polyp boundaries difficult to distinguish and works like U-Net++ [PERSON] et al. (2015). ResUnet++ [PERSON] et al. (2015) aims to address these complexities. However, U-Net and its variants usually use a symmetric encoder-decoder structure, which is able to process the global information of the image, but there is no specialized mechanism to enhance the edge details, and the local feature maps may lose some critical edge information in the decoding stage, which may affect the accurate segmentation of edge details. In addition, although the U-like structure of the network improves the fusion of features to a certain extent by skip connection, the capture of multi-scale edge information is still limited. Recently, some methods have been proposed to try to solve these problems. PraNet [PERSON] et al. (2015) uses a reverse attention module to mine boundary clues, establish the relationship between region and boundary clues, and calibrate misaligned predictions through a cyclic cooperation mechanism between regions and boundaries to improve segmentation accuracy. CPFNet [PERSON] et al. (2015) employs a context pyramid fusion network to provide multiple levels of global context to the decoder through reconstructed skip connections. TGA-Net [PERSON] et al. (2015) leverages a text-guided attention mechanism to enhance polyp segmentation by integrating natural language descriptions of polyp size and quantity with image features. However, the lack of standardized text information limits its effectiveness. Attention mechanisms have gained prominence across various domains in recent years. While these mechanisms show potential for improving polyp segmentation, challenges persist due to foreground-background imbalance and edge blurring. Overemphasis on attention mechanisms may introduce irrelevant noise, adversely impacting model accuracy.