Over the last decade, deep learning (DL) has contributed to a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID-19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This article presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL-based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.

中文翻译：

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肺部影像人工智能研究进展

在过去的十年中，深度学习 (DL) 推动了计算机视觉和图像识别领域的范式转变，为在研究和工业应用中使用人工智能创造了广泛的机会。深度学习在医学成像应用中得到了广泛的研究，包括与肺部疾病相关的应用。慢性阻塞性肺病、哮喘、肺癌、肺炎以及最近的 COVID-19 都是常见的肺部疾病，影响着世界近 7.4% 的人口。肺部成像已被广泛研究，以提高我们对疾病病因的理解以及疾病进展和临床结果的早期诊断和评估。深度学习已广泛应用于解决各种肺部图像处理挑战，包括分类、识别、配准和分割。本文对肺部疾病、成像在转化和临床肺部研究中的作用以及不同深度学习架构和方法在肺部成像中的应用进行了调查，重点是基于深度学习的主要肺部解剖结构（如肺体积、肺叶、肺脏）的分割。血管、气道以及与肺部疾病相关的胸部肌肉骨骼解剖结构。