Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19

By Feng Shi, Jun Wang, Jun Shi, Zi-xiang Wu, Qian Wang, Zhenyu Tang, Kelei He, Yinghuan Shi, Dinggang Shen. IEEE Reviews in Biomedical Engineering, 2020

1,123 citations recorded by the source corpus.

The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delineation of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4e95ce827049a350819c5c87caf992f27f9ff792

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In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images (2020, methodology): “As an important complement to ( RT-PCR tests, the radiological imaging techniques, e.g., Xrays and computed tomography (CT), have also demonstrated effectiveness in both current diagnosis, including follow-up assessment and evaluation of disease evolution [5] , [6] .”
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Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia (2020, methodology): “For more related works, a recent review paper of automatic segmentation methods on COVID-19 could be found in [43].”
Concealed Object Detection (2021, background): “More details on this application can be found in the recent segmentation model [8] and survey paper [92].”
A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images (2020, background): “pneumonia lesions from medical images [3].”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “AI-based tools are developed to support the assessment of disease severity, and recently, they start to provide tools for assessing treatment and predicting treatment success [242], [243].”
Leveraging Data Science to Combat COVID-19: A Comprehensive Review (2020, background): “To diagnose COVID-19, two medical imaging modalities (CT and X-ray) have been experimented with [125], which we discuss below.”
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network (2020, methodology): “s. Several reviews 1 The developed code is available at https://github.com/asmaa4may/DeTraC COVId19. 4 Asmaa Abbas et al. have been carried out to highlight recent contributions to COVID-19 detection [8,15,24]. For example, in [33], a CNN was applied based on Inception network to detect COVID-19 dis”
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications (2020, background): “A survey on deep learning techniques for COVID-19 diagnosis reveals that U-net is one of the primary models of choice for segmentation related tasks [270].”
Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound (2020, background): “In the wake of the current pandemic, recent works have focused on the detection of COVID-19 from chest CT [25], [26].”