基于深度学习的肺部CT图像病灶区域分割研究综述  

Review of Lung CT Image Lesion Region Segmentation Based on Deep Learning

作　　者：李小童 马素芬[1,2] 生慧 魏国辉[1] 李欣桐 LI Xiaotong;MA Sufen;SHENG Hui;WEI Guohui;LI Xintong(College of Intelligence and Information Engineering,Shandong University of Traditional Chinese Medicine,Jinan 250355,China;Academic Affairs Office of Shandong University of Traditional Chinese Medicine,Jinan 250355,China)

机构地区：[1]山东中医药大学智能与信息工程学院,济南250355 [2]山东中医药大学教务处,济南250355

出　　处：《计算机工程与应用》2025年第4期25-42,共18页Computer Engineering and Applications

基　　金：山东省自然科学基金面上项目(ZR2022MH203);山东省研究生优质教育教学资源项目(SDYKC2023044);山东省中医药科技项目(2020M005);山东省教育厅教学改革研究项目(Z2022152)。

摘　　要：肺癌严重威胁人们的生命健康。肺部CT图像病灶区域形态复杂多样,实现高精度的肺部CT图像病变区域分割,成为计算机辅助诊断领域的一个极具挑战性的关键问题。基于深度学习的肺部病灶区域分割不仅可以帮助医生快速、准确地诊断出早期肺癌,而且对于肺癌的治疗也具有重要的临床价值。为了深入研究肺部病灶区域分割技术,介绍了常用的数据集及评价指标;重点从基于卷积神经网络、基于U-Net模型、基于生成对抗网络三个方面对深度学习肺部病灶区域分割模型进行了综述;结合具体实验总结了近5年国内外研究的创新点,对比分析了各个模型的分割性能;最后总结了各类模型的优缺点,展望了该领域的未来发展方向。Lung cancer poses a serious threat to people’s lives and health.The morphology of lesion areas in lung CT images is complex and diverse,and achieving high-precision segmentation of lesion areas in lung CT images has become a highly challenging key issue in the field of computer-aided diagnosis.The segmentation of lung lesion regions based on deep learning not only helps doctors diagnose early lung cancer quickly and accurately,but also has important clinical value for the treatment of lung cancer.In order to conduct in-depth research on lung lesion segmentation techniques,common datasets and evaluation indicators are introduced.The deep learning lung lesion regions segmentation models are reviewed in three aspects:segmentation model based on convolutional neural network,segmentation model based on U-Net model,and segmentation model based on generative adversarial network.The innovative points of domestic and foreign research over the past 5 years are summarized through specific experiments.The segmentation performance of various models is compared and analyzed.The advantages and disadvantages of various models are summarized,and the development direction in this field is discussed.

关 键 词：深度学习 肺部病灶区域分割 卷积神经网络 U-Net模型 生成对抗网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]