计算机工程与应用2025，Vol.61Issue(4)：25-42,18.DOI:10.3778/j.issn.1002-8331.2403-0315
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
摘要
Abstract
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 seg-mentation 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 com-pared 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Key words
deep learning/segmentation of lung lesion areas/convolutional neural network/U-Net model/generative adversarial network分类
信息技术与安全科学引用本文复制引用
李小童,马素芬,生慧,魏国辉,李欣桐..基于深度学习的肺部CT图像病灶区域分割研究综述[J].计算机工程与应用,2025,61(4):25-42,18.基金项目
山东省自然科学基金面上项目(ZR2022MH203) （ZR2022MH203）
山东省研究生优质教育教学资源项目(SDYKC2023044) （SDYKC2023044）
山东省中医药科技项目(2020M005) （2020M005）
山东省教育厅教学改革研究项目(Z2022152). （Z2022152）
