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  • Title: [Research progress on computed tomography image detection and classification of pulmonary nodule based on deep learning].
    Author: Wang J, Lin L, Zhao S, Wu X, Wu S.
    Journal: Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2019 Aug 25; 36(4):670-676. PubMed ID: 31441270.
    Abstract:
    Computer-aided diagnosis based on computed tomography (CT) image can realize the detection and classification of pulmonary nodules, and improve the survival rate of early lung cancer, which has important clinical significance. In recent years, with the rapid development of medical big data and artificial intelligence technology, the auxiliary diagnosis of lung cancer based on deep learning has gradually become one of the most active research directions in this field. In order to promote the deep learning in the detection and classification of pulmonary nodules, we reviewed the research progress in this field based on the relevant literatures published at domestic and overseas in recent years. This paper begins with a brief introduction of two widely used lung CT image databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and Data Science Bowl 2017. Then, the detection and classification of pulmonary nodules based on different network structures are introduced in detail. Finally, some problems of deep learning in lung CT image nodule detection and classification are discussed and conclusions are given. The development prospect is also forecasted, which provides reference for future application research in this field. 基于电子计算机断层扫描（CT）影像的计算机辅助诊断可实现对肺结节的检测与分类，提高早期肺癌的生存率，具有重要临床意义。近年来，随着医疗大数据与人工智能技术的飞速发展，基于深度学习算法的肺癌辅助诊断已逐渐成为该领域最为活跃的研究方向之一。为了进一步推动深度学习算法在肺结节检测和分类中的研究，本文结合近年国内外发表的相关文献，对该领域的研究进展进行综述。首先，简要介绍了两大广泛使用的肺 CT 影像数据库：肺部图像数据库联盟与图像数据库资源计划（LIDC-IDRI）和 2017 数据科学杯（Data Science Bowl 2017）。然后，对多种不同深度网络架构的肺结节检测与分类研究分别进行详细的介绍。最后，讨论了深度学习在结节检测和分类中面临的一些问题并给出结论，并对发展前景进行了展望，为今后该领域的应用研究提供参考。.
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