基于深度学习的肺结节CT图像分割与分类研究综述

doi:10.3778/j.issn.1002-8331.2411-0136

摘要/Abstract

摘要： 肺癌是最致命的癌症类型之一，而肺结节作为肺癌的早期症状，严重威胁人们的生命健康。基于深度学习的肺结节CT图像的分割与分类技术，可以帮助医生快速、准确地诊断出早期结节，对于肺癌的治疗具有重要的临床价值。为了深入研究肺结节CT图像分割与分类技术，介绍了常用数据集及评价指标；着重从两个方面对深度学习肺结节CT图像分割与分类模型进行综述：基于U-Net的单网络结构模型与多网络结构模型分割方法、基于卷积神经网络的特征融合和纹理特征分类方法；结合具体实验总结了近五年国内外研究的创新点，以及各类模型的优缺点。最后，展望了该领域的未来发展方向，以期为该领域的后续研究提供理论参考和借鉴。

关键词: 肺结节, 深度学习, 计算机辅助诊断, 医学图像, 卷积神经网络, U-Net模型

Abstract: Lung cancer is one of the deadliest forms of cancer, and lung nodules, as early symptoms of the disease, pose a serious threat to people’s lives and health. The segmentation and classification of lung nodule CT images based on deep learning can help doctors quickly and accurately diagnose early-stage nodules, which has significant clinical value for the treatment of lung cancer. To explore the segmentation and classification techniques for lung nodule CT images, firstly, common datasets and evaluation indicators are introduced. Secondly, the deep learning models for lung nodule CT image segmentation and classification are reviewed from two perspectives: single-network models and multi-network models based on U-Net, feature fusion and texture feature classification methods using convolutional neural networks. The innovative points of domestic and foreign research over the past 5 years are summarized through specific experiments, and the advantages and disadvantages of various models have also been outlined. Finally, the development direction in this field is discussed, offering theoretical guidance and reference for future research in this area.

Key words: lung nodules, deep learning, computer-aided diagnosis, medical images, convolutional neural networks, U-Net model

古力米热·阿吾旦, 叶俊翔, 玛依拉·阿不都克力木, 王梦飞, 哈里旦木·阿布都克里木, 阿布都克力木·阿布力孜. 基于深度学习的肺结节CT图像分割与分类研究综述[J]. 计算机工程与应用, 2025, 61(15): 14-35.

Awudan Gulimire, YE Junxiang, Abudukelimu Mayila, WANG Mengfei, Abudukelimu Halidanmu, Abulizi Abudukelimu. Review of Deep Learning-Based Segmentation and Classification of CT Images of Lung Nodules[J]. Computer Engineering and Applications, 2025, 61(15): 14-35.

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