The crucial task of polyp recognition in medical imaging plays a pivotal role in the early detection and prevention of colorectal cancer. Semantic segmentation, particularly utilizing sophisticated deep learning models such as U-Net, has demonstrated promising results in the realm of polyp segmentation. However, the traditional U-Net structure sometimes grapples with accurately delineating the edges of polyps, which subsequently impacts the overall performance of segmentation. To address this issue, the current study introduces a novel approach by proposing a modified framework of U-Net, equipped with an enhanced edge loss function. This function is designed to ameliorate the accuracy of segmentation within polyp images. The aim is to elevate the model’s capacity to capture intricate details, specifically the edges, which is an area where standard U-Net structures often falter. Experimental outcomes of this study serve to underscore the effectiveness of the proposed approach in accomplishing superior segmentation of edges and improved overall performance in polyp recognition. By successfully tackling the challenges inherent to polyp edge segmentation, the modified U-Net model contributes significantly towards more precise diagnostic systems in the field of medical imaging. Consequently, this research is poised to make a valuable contribution to advancements in the prevention and early detection of colorectal cancer.

中文翻译：

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通过增强 U-Net 架构改进多边形图像分割

医学影像中息肉识别的关键任务在结直肠癌的早期发现和预防中发挥着关键作用。语义分割，特别是利用 U-Net 等复杂的深度学习模型，在息肉分割领域已经展示了有希望的结果。然而，传统的 U-Net 结构有时难以准确描绘息肉的边缘，从而影响分割的整体性能。为了解决这个问题，当前的研究引入了一种新颖的方法，提出了一种改进的 U-Net 框架，配备了增强的边缘损失函数。该功能旨在提高息肉图像分割的准确性。目的是提高模型捕获复杂细节的能力，特别是边缘，这是标准 U-Net 结构经常出现问题的区域。这项研究的实验结果强调了所提出的方法在实现卓越的边缘分割和提高息肉识别中的整体性能方面的有效性。通过成功解决息肉边缘分割固有的挑战，修改后的 U-Net 模型为医学成像领域更精确的诊断系统做出了重大贡献。因此，这项研究将为结直肠癌的预防和早期检测的进步做出宝贵的贡献。