Min Yuqin, Hu Liangyun, Wei Long, Nie Shengdong
Institute of Medical Imaging Engineering, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
Center for Functional Neurosurgery, RuiJin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People's Republic of China.
Phys Med Biol. 2022 Mar 7;67(6). doi: 10.1088/1361-6560/ac568e.
Computer-aided detection (CADe) technology has been proven to increase the detection rate of pulmonary nodules that has important clinical significance for the early diagnosis of lung cancer. In this study, we systematically review the latest techniques in pulmonary nodule CADe based on deep learning models with convolutional neural networks in computed tomography images. First, the brief descriptions and popular architecture of convolutional neural networks are introduced. Second, several common public databases and evaluation metrics are briefly described. Third, state-of-the-art approaches with excellent performances are selected. Subsequently, we combine the clinical diagnostic process and the traditional four steps of pulmonary nodule CADe into two stages, namely, data preprocessing and image analysis. Further, the major optimizations of deep learning models and algorithms are highlighted according to the progressive evaluation effect of each method, and some clinical evidence is added. Finally, various methods are summarized and compared. The innovative or valuable contributions of each method are expected to guide future research directions. The analyzed results show that deep learning-based methods significantly transformed the detection of pulmonary nodules, and the design of these methods can be inspired by clinical imaging diagnostic procedures. Moreover, focusing on the image analysis stage will result in improved returns. In particular, optimal results can be achieved by optimizing the steps of candidate nodule generation and false positive reduction. End-to-end methods, with greater operating speeds and lower computational consumptions, are superior to other methods in CADe of pulmonary nodules.
计算机辅助检测（CADe）技术已被证明可提高肺结节的检测率，这对肺癌的早期诊断具有重要的临床意义。在本研究中，我们基于计算机断层扫描图像中具有卷积神经网络的深度学习模型，系统地综述了肺结节CADe的最新技术。首先，介绍了卷积神经网络的简要描述和流行架构。其次，简要描述了几个常见的公共数据库和评估指标。第三，选择了具有优异性能的最先进方法。随后，我们将临床诊断过程和肺结节CADe的传统四个步骤合并为两个阶段，即数据预处理和图像分析。此外，根据每种方法的渐进评估效果，突出了深度学习模型和算法的主要优化，并增加了一些临床证据。最后，对各种方法进行了总结和比较。期望每种方法的创新或有价值的贡献能够指导未来的研究方向。分析结果表明，基于深度学习的方法显著改变了肺结节的检测，这些方法的设计可以受到临床影像诊断程序的启发。此外，专注于图像分析阶段将带来更好的回报。特别是，通过优化候选结节生成和减少假阳性的步骤可以实现最佳结果。在肺结节CADe中，端到端方法具有更高的运行速度和更低的计算消耗，优于其他方法。
