摘要
肺癌是世界上死亡率最高的恶性肿瘤疾病,对其进行早期诊断可以显著提高肺癌患者的生存率。深度学习能够提取医学图像的隐含层特征,并完成医学图像的分类及分割任务,因此应用深度学习方法实现肺结节的早期诊断任务成为研究热点。首先对肺结节诊断领域广泛使用的几个数据库进行介绍,然后结合近几年国内外发表的相关文献整理了深度学习框架应用于肺结节分割和分类的最新研究进展,总结并分析了各类算法的基本思想、网络架构形式、代表性改进方案以及优缺点等。最后讨论了深度学习在肺结节诊断过程中面临的一些问题并给出结论,并对发展前景进行了展望,以期为今后该领域的应用研究提供参考,从而加快该领域研究的成熟和临床落地应用。
Lung cancer is the malignant tumor with the highest mortality rate in the world.Its early diagnosis can remarkably improve the survival rate of lung cancer patients.Deep learning can extract the hidden layer features of medical images and can complete the classification and segmentation of medical images.The application of deep learning methods for the early diagnosis of lung nodules has become a key point of research.This article introduces several databases commonly used in the field of lung nodule diagnosis and combines the relevant literature recently published at home and abroad to classify the latest research progress and summarize and analyze the application of deep learning frameworks for lung nodule image segmentation and classification.The basic ideas of various algorithms,network architecture forms,representative improvement schemes,and a summary of advantages and disadvantages are presented.Finally,some problems encountered while using deep learning for the diagnosis of pulmonary nodules,conclusions,and the development prospects are discussed.This study is expected to provide a reference for future research applications and accelerate the maturity of research and clinical applications in the concerned field.
作者
曹斌
杨锋
马金刚
Cao Bin;Yang Feng;Ma Jingang(Shandong Provincial Hospital of Traditional Chinese Medicine,Jinan,Shandong 250000,China;School of Intelligence and Information Engineering,Shandong University of Traditional Chinese Medicine,Jinan,Shandong 250355,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第16期96-109,共14页
Laser & Optoelectronics Progress
基金
国家自然科学基金(81473708,81973981,82074579)
山东省重点研发计划项目(2018GSF118105)
山东省重点研发计划(软科学)项目(2019RKB14090)。
关键词
图像处理
肺结节
卷积神经网络
计算机辅助诊断
深度学习
分割
分类
image processing
lung nodules
convolutional neural network
computer-aided diagnosis
deep learning
segmentation
classification
