Tang Rui, Zhao Hejing, Tong Yao, Mu Ruihui, Wang Yuqiang, Zhang Shuhao, Zhao Yao, Wang Weidong, Zhang Min, Liu Yilin, Gao Jianbo
Department of Orthopedics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China.
Research Center on Flood and Drought Disaster Reduction of Ministry of Water Resource, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China.
Sci Rep. 2025 Feb 10;15(1):4961. doi: 10.1038/s41598-025-88475-6.
Gastrointestinal polyps are observed and treated under endoscopy, so there presents significant challenges to advance endoscopy imaging segmentation of polyps. Current methodologies often falter in distinguishing complex polyp structures within diverse (mucosal) tissue environments. In this paper, we propose the Frequency Attention-Embedded Network (FAENet), a novel approach leveraging frequency-based attention mechanisms to enhance polyp segmentation accuracy significantly. FAENet ingeniously segregates and processes image data into high and low-frequency components, enabling precise delineation of polyp boundaries and internal structures by integrating intra-component and cross-component attention mechanisms. This method not only preserves essential edge details but also refines the learned representation attentively, ensuring robust segmentation across varied imaging conditions. Comprehensive evaluations on two public datasets, Kvasir-SEG and CVC-ClinicDB, demonstrate FAENet's superiority over several state-of-the-art models in terms of Dice coefficient, Intersection over Union (IoU), sensitivity, and specificity. The results affirm that FAENet's advanced attention mechanisms significantly improve the segmentation quality, outperforming traditional and contemporary techniques. FAENet's success indicates its potential to revolutionize polyp segmentation in clinical practices, fostering diagnosis and efficient treatment of gastrointestinal polyps.
胃肠道息肉在内镜检查下进行观察和治疗，因此推进息肉的内镜成像分割面临重大挑战。当前的方法在区分不同（黏膜）组织环境中的复杂息肉结构时常常遇到困难。在本文中，我们提出了频率注意力嵌入网络（FAENet），这是一种利用基于频率的注意力机制来显著提高息肉分割准确性的新颖方法。FAENet巧妙地将图像数据分离并处理为高频和低频分量，通过整合组件内和跨组件注意力机制，能够精确描绘息肉边界和内部结构。该方法不仅保留了重要的边缘细节，还精心优化了学习到的表示，确保在各种成像条件下都能进行稳健的分割。在两个公共数据集Kvasir-SEG和CVC-ClinicDB上的综合评估表明，FAENet在Dice系数、交并比（IoU）、灵敏度和特异性方面优于几种先进的模型。结果证实，FAENet先进的注意力机制显著提高了分割质量，优于传统和现代技术。FAENet的成功表明其有潜力彻底改变临床实践中的息肉分割，促进胃肠道息肉的诊断和有效治疗。
