Affiliation:
1. Department of Electrical and Computer Engineering University of Iowa Iowa City Iowa USA
2. Department of Radiology University of Iowa Iowa City Iowa USA
3. Department of Internal Medicine University of Iowa Iowa City Iowa USA
Abstract
AbstractOver 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.This article is categorized under:
Application Areas > Health Care
Technologies > Artificial Intelligence
Technologies > Computational Intelligence
Application Areas > Science and Technology
Funder
National Institutes of Health
Cited by
5 articles.
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