MA_MultiResUnet:一种基于改进MultiResUnet的CT影像肺结节分割方法  

MA_MULTIRESUNET:A SEGMENTATION METHOD OF PULMONARY NODULES IN CT IMAGES BASED ON IMPROVED MULTIRESUNET

作　　者：卢伟 帅仁俊 赵宸 Lu Wei;Shuai Renjun;Zhao Chen(College of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,Jiangsu,China)

机构地区：[1]南京工业大学计算机科学与技术学院,江苏南京211816

出　　处：《计算机应用与软件》2025年第4期237-244,共8页Computer Applications and Software

基　　金：国家自然科学基金项目(61701222)。

摘　　要：肺癌在形成肿瘤之前,往往会以肺结节的形态出现,因此及时对肺结节做出正确诊断对提高患者的生存率有重大的意义。提出一种基于MA_MultiResUnet的CT(Computed Tomography)影像肺结节分割方法来辅助医生诊断肺结节,该方法通过重新定义模型中跳跃连接结构来进一步获得具有多尺度空间信息且重要通道特征突出的特征图以及在解码器中引入通道注意力模块来进行特征校准,从而改善网络对肺结节的分割表现。数据集采用LIDC-IDRI公开数据集,将该方法在预处理后的数据集上进行评估,实验结果表明,MA_MultiResUnet的Recall、Dice、MIoU表现分别达到85.76%、84.24%、86.99%,较已有方法有显著的提升。Before lung cancer forms a tumor,it often appears in the form of lung nodules.Therefore,a correct diagnosis of lung nodules in time is of great significance to improve the survival rate of patients.This paper proposes a segmentation method of pulmonary nodules in CT images based on MA_MultiResUnet to assist doctors in diagnosing lung nodules.This method further obtained the feature map with multi-scale spatial information and prominent important channel features by redefining the skip connection structure in the model,and the channel attention module was introduced into the decoder to perform feature calibration,so as to improve the network's segmentation performance of lung nodules.The dataset used the LIDC-IDRI public dataset,and the proposed method was evaluated on the pre-processed dataset.The experimental results show that the Recall,Dice and MIoU performance of MA_MultiResUnet reach 85.76%,84.24%,86.99%,respectively,and the segmentation performance is better than the existing ones.

关 键 词：MA_MultiResUnet CT影像 肺结节 分割 跳跃连接 注意力 特征校准 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] R734.2[自动化与计算机技术—计算机科学与技术]