Abstract
Early detection of colorectal polyps through endoscopic colonoscopy is crucial in reducing colorectal cancer mortality. While automated polyp segmentation has been explored to enhance detection accuracy and efficiency, challenges remain in achieving precise boundary delineation, particularly for small or flat polyps. In this work, we propose a novel U-Net-based segmentation framework specifically optimized for real-world endoscopic colonoscopy data. Unlike conventional approaches, our method leverages high-resolution frames with pixel-level ground-truth annotations to achieve superior segmentation performance. The U-Net architecture, with its symmetric encoder-decoder design and skip connections, is further adapted to enhance both high-level contextual understanding and fine-grained detail preservation. Our model has been rigorously evaluated on a real-world dataset, demonstrating state-of-the-art accuracy in polyp boundary segmentation, even in challenging cases. By improving detection consistency and reducing observer variability, our approach provides a robust tool to support gastroenterologists in clinical decision-making. Beyond real-time clinical applications, this work contributes to advancing automated and standardized polyp detection, paving the way for more reliable AI-assisted endoscopic analysis.