摘要
由于息肉图像的自动分割病灶区域大小不一和边界模糊,从而导致分割精度较低.针对这两个问题,本文提出先定位后逐步精细的渐进式消减网络(Progressive Reduction Network,PRNet).该网络采用Res2Net提取病灶区域特征,利用多尺度跨级融合模块将注意融合机制与跨级特征结合,有效应对病灶区域多尺度问题,提升定位准确度.在自上而下恢复图像分辨率的过程中,引入不确定区域处理模块和多尺度上下文感知模块.前者通过设定递减的阈值逐步挖掘息肉边缘信息,增强边缘细节特征的识别能力;后者则进一步探索病灶区域周围潜在的上下文语义,提升模型的整体表征能力.此外,本算法还设计了一个简单的特征过滤模块,用于筛选编码器特征中的有效信息.在Kvasir-SEG、CVC-Clinic和ETIS数据集上的实验结果表明,所提算法的Dice系数分别达到了92.09%、93.05%和74.19%,优于现有的息肉分割算法,展示出了较好的鲁棒性和泛化性.
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.
作者
何东之
肖杏梅
李韫昱
薛永乐
李雲奇
HE Dongzhi;XIAO Xingmei;LI Yunyu;XUE Yongle;LI Yunqi(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;The First Medical Center of Chinese People′s Liberation Army(PLA)General Hospital,Beijing 100039,China)
出处
《湖南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2024年第6期40-51,共12页
Journal of Hunan University:Natural Sciences
基金
国家自然科学基金资助项目(62005045)
教育部产学合作协同育人项目(220702278135421)。
关键词
医学图像处理
息肉分割
结肠镜
不确定区域
多尺度
medical image segmentation
polyp segmentation
colonoscopy
uncertain area
multi-scale
