基于深度学习的肺部CT图像病灶区域分割研究综述

doi:10.3778/j.issn.1002-8331.2403-0315

摘要/Abstract

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。

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

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 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.

Key words: deep learning, segmentation of lung lesion areas, convolutional neural network, U-Net model, generative adversarial network

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.

LI Xiaotong, MA Sufen, SHENG Hui, WEI Guohui, LI Xintong. Review of Lung CT Image Lesion Region Segmentation Based on Deep Learning[J]. Computer Engineering and Applications, 2025, 61(4): 25-42.

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