基于局部-全局特征交互的双分支结肠息肉分割网络  

Two-branch Colon Polyp Segmentation Network Based on Local-global Feature Interaction

作　　者：徐康业 陈建平 陈平华[1] XU Kang-Ye;CHEN Jian-Ping;CHEN Ping-Hua(School of Computer Science and Technology,Guangdong University of Technology,Guangzhou 510006,China;Zhaoqing Municipal Education Bureau,Zhaoqing 526020,China)

机构地区：[1]广东工业大学计算机学院,广州510006 [2]肇庆市教育局,肇庆526020

出　　处：《计算机系统应用》2024年第4期133-142,共10页Computer Systems & Applications

基　　金：广东省重点领域研发计划(2019A050510041,2020B0101100001)。

摘　　要：大小、形状、颜色、纹理的多变性以及肠壁分界模糊给结肠息肉的分割带来巨大挑战.针对单分支网络连续采样操作造成部分细节信息丢失以及不同层次特征信息无法交互进而导致分割效果不佳的问题,提出一种基于局部-全局特征交互的双分支结肠息肉分割网络.网络采用CNN与Transformer双分支结构,逐层捕获息肉局部细节特征与全局语义特征;为充分利用不同层级、不同尺度特征信息的互补性,利用深层语义特征对浅层细节特征的引导与增强,设计特征协同交互模块,动态感知并聚合跨层次特征交互信息;为强化病变区域特征,抑制背景噪声,设计特征增强模块,应用空间与通道注意力机制强化息肉病变区域特征,同时采用结合注意力门的跳跃连接机制进一步突出边界信息,提高边缘区域的分割精度.实验表明,所提出网络在多个息肉分割数据集上取得的mDice与mIoU分数均优于基线网络,具有更高的分割准确率和稳定性.The variability in size,shape,color,and texture,along with the blurring demarcation of the bowel wall,presents a significant challenge in colon polyp segmentation.The detail information loss and lack of interaction between different feature levels due to continuous sampling in single-branch networks lead to poor segmentation results.To address this problem,this study proposes a two-branch colon polyp segmentation network based on local-global feature interaction.The network utilizes a dual branch structure consisting of CNN and Transformer,systematically capturing the precise local details and the global semantic features of the polyp in each layer.To make full use of the complementary nature of feature information at different levels and scales,and to utilize the guidance and enhancement of shallow detailed features by deep semantic features,the paper designs the feature cooperative interaction module to dynamically sense and aggregate cross-level feature interaction information.To enhance the feature of the polyp lesion region while reducing background noise,the feature enhancement module utilizes spatial and channel attention mechanisms.Additionally,the skip-connection mechanism in conjunction with the attention gate further highlights boundary information,resulting in improved edge region segmentation accuracy.Experiments show that the proposed network achieves better mDice and mIoU scores than the baseline network on multiple polyp segmentation datasets,with higher segmentation accuracy and stability.

关 键 词：结肠息肉分割 卷积神经网络 TRANSFORMER 双分支结构 协同交互 多尺度特征 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] R735.34[自动化与计算机技术—计算机科学与技术]