湖南大学学报（自然科学版）2024，Vol.51Issue(6)：40-51,12.DOI:10.16339/j.cnki.hdxbzkb.2024265
PRNet:渐进式消减不确定区域的息肉分割网络
PRNet:Progressive Reduction Network for Polyp Segmentation in Uncertain Areas
摘要
Abstract
Automatic segmentation of polyp images usually results in low segmentation accuracy due to the various sizes of lesion regions and blurry boundaries.Based on these two perspectives,a novel Progressive Reduction Network(PRNet)is proposed,which first locates polyps and then gradually refines their boundaries.The network utilizes Res2Net to extract features from the lesion region and leverages the multi-scale cross-level fusion module to improve localization accuracy.By combining the attention fusion mechanism with cross-level features in this module,the network can effectively solve the issue of multi-scale lesion areas.Furthermore,PRNet combines an uncertain region processing module and a multi-scale context-aware module when restoring image resolution from top to bottom.The former gradually mines polyp edge information by setting decreasing thresholds to enhance the recognition of edge detail features,while the latter,to improve the overall representation capability of the model,further explores the inherent potential contextual semantics of lesion regions.In addition,a simple feature filtering module is designed in this algorithm to filter the valid information in the encoder features.Experimental results on the Kvasir-SEG,CVC-Clinic,and ETIS datasets show that the Dice coefficients of the algorithm reach 92.09%,93.05%,and 74.19%,respectively.Compared with other existing polyp segmentation algorithms,PRNet outperforms them and demonstrates its superior robustness and generalization.关键词
医学图像处理/息肉分割/结肠镜/不确定区域/多尺度Key words
medical image segmentation/polyp segmentation/colonoscopy/uncertain area/multi-scale分类
信息技术与安全科学引用本文复制引用
何东之,肖杏梅,李韫昱,薛永乐,李雲奇..PRNet:渐进式消减不确定区域的息肉分割网络[J].湖南大学学报（自然科学版）,2024,51(6):40-51,12.基金项目
国家自然科学基金资助项目(62005045),National Natural Science Foundation of China(62005045) （62005045）
教育部产学合作协同育人项目(220702278135421),University-Industry Collaborative Education Program,Ministry of Education,China(220702278135421) （220702278135421）
