基于改进U-Net网络的肺部CT图像分割算法研究  

Research on lung CT image segmentation algorithm based on the improved U-Net network

作　　者：卢小燕 袁文昊 徐杨 LU Xiaoyan;YUAN Wenhao;XU Yang(College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学大数据与信息工程学院,贵阳550025

出　　处：《智能计算机与应用》2023年第7期179-186,共8页Intelligent Computer and Applications

基　　金：贵州省科技计划项目(黔科合支撑[2021]一般176)。

摘　　要：针对肺部疾病的诊疗,从计算机断层扫描(Computed Tomography,CT)图像中自动检测肺部病灶,对监测疾病进展和进一步临床治疗具有重要意义。为此,基于U-Net网络,提出新型的肺部CT图像分割网络LG-Net。利用带有注意力模块的跳层连接对病灶区域提取边缘轮廓信息;其次,引入多级残差卷积与特征融合模块,弥补网络中的特征损失问题。在公开数据集上的实验表明,LG-Net提高了肺部CT图像的分割精度,相较于传统的U-Net算法具有更优异的分割性能。For the diagnosis and treatment of lung diseases,automatic detection of lung infection from Computed Tomography(CT)images is of great significance in monitoring disease progression and further clinical treatment.Therefore,based on U-Net,a new lung CT image segmentation network LG-Net is proposed.Firstly,the edge contour information is extracted from the focal region by using the skip connection with the attention module.Secondly,the multi-level residual convolution and feature fusion modules are introduced to make up for feature loss in the network.Experiments on public datasets show that LG-Net improves the segmentation accuracy of lung CT images and has better segmentation performance than traditional U-Net algorithm.

关 键 词：病灶分割 多级残差卷积 注意力模块 特征融合模块 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]