融合多尺度特征和双分支并行的肺结节图像分割网络  

Pulmonary Nodule Image Segmentation Network Integrating Multi-scale Features and Dual-branch Parallelism

作　　者：王超学[1] 王磊[1] WANG Chao-Xue;WANG Lei(School of Information and Control Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China)

机构地区：[1]西安建筑科技大学信息与控制工程学院,西安710055

出　　处：《计算机系统应用》2025年第4期166-174,共9页Computer Systems & Applications

基　　金：国家自然科学基金面上项目(62072363,32471597)。

摘　　要：肺结节图像的准确分割对于肺癌的早期诊断具有重要意义,针对肺结节图像尺度多样、边缘模糊导致特征提取不充分和细节信息丢失问题,本文提出一种融合多尺度特征和双分支并行的肺结节图像分割网络RAVR-UNet.首先,针对U-Net网络在编码阶段无法充分提取肺结节特征,采用双分支并行特征聚合网络提取肺结节图像中的特征信息,减少特征编码时的信息损失.其次,通过引入Agent_ViT模块,在保持线性计算的基础上,增强全局信息建模能力.然后,为恢复下采样期间丢失的肺结节空间信息,在解码阶段加入多尺度特征融合模块.最后,设计混合损失函数以缓解肺结节图像分割任务中正负样本不平衡问题.在LIDC-IDRI公开数据集上的实验结果表明,所提网络的相似系数、交并比分别达到93.15%、87.63%,优于主流肺结节分割算法且分割结果更接近真实值.Accurate image segmentation of pulmonary nodules is of great significance for the early diagnosis of lung cancer.To solve the problem of insufficient feature extraction and detail loss caused by multiple scales and blurred edges of pulmonary nodules image,this study proposes a pulmonary nodule image segmentation network named RAVR-UNet,which incorporates multi-scale features and double-branch parallel.Firstly,given the inability of the U-Net network to fully extract pulmonary nodule features in the coding stage,a double-branch parallel feature aggregation network is used to extract the feature information from pulmonary nodule images to reduce the information loss during feature coding.Secondly,the Agent_ViT module is introduced to enhance the capability of global information modeling while maintaining linear computation.Then,to recover the lost pulmonary nodule spatial information during subsampling,a multi-scale feature fusion module is added in the decoding stage.Finally,a mixed loss function is designed to alleviate the imbalance between positive and negative samples in the pulmonary nodule image segmentation task.Experimental results on the LIDC-IDRI public dataset show that the similarity coefficient and intersection over union(IoU)of the proposed network reach 93.15%and 87.63%,respectively,which is better than the mainstream pulmonary nodal segmentation algorithms,and the segmentation results are closer to the real values.

关 键 词：肺结节图像分割 多尺度特征融合 U-Net VIT 代理注意力 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]