scholarly journals Chest CT Images for COVID-19: Radiologists and Computer-Based Detection

2021 ◽  
Vol 8 ◽  
Author(s):  
Qingli Dou ◽  
Jiangping Liu ◽  
Wenwu Zhang ◽  
Yanan Gu ◽  
Wan-Ting Hsu ◽  
...  
Keyword(s):  
Image Analysis ◽  
Ct Images ◽  
Chest Ct ◽  
Viral Pneumonia ◽  
Image Analysis System ◽  
Ct Image ◽  
Significant Difference ◽  
Computer Based ◽  
Analysis System ◽  
Novel Coronavirus

BackgroundCharacteristic chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) was added as a diagnostic criterion in the Chinese National COVID-19 management guideline. Whether the characteristic findings of Chest CT could differentiate confirmed COVID-19 cases from other positive nucleic acid test (NAT)-negative patients has not been rigorously evaluated.PurposeWe aim to test whether chest CT manifestation of 2019 novel coronavirus (COVID-19) can be differentiated by a radiologist or a computer-based CT image analysis system.MethodsWe conducted a retrospective case-control study that included 52 laboratory-confirmed COVID-19 patients and 80 non-COVID-19 viral pneumonia patients between 20 December, 2019 and 10 February, 2020. The chest CT images were evaluated by radiologists in a double blind fashion. A computer-based image analysis system (uAI System, Lianying Inc., Shanghai, China) detected the lesions in 18 lung segments defined by Boyden classification system and calculated the infected volume in each segment. The number and volume of lesions detected by radiologist and computer system was compared with Chi-square test or Mann-Whitney U test as appropriate.ResultsThe main CT manifestations of COVID-19 were multi-lobar/segmental peripheral ground-glass opacities and patchy air space infiltrates. The case and control groups were similar in demographics, comorbidity, and clinical manifestations. There was no significant difference in eight radiologist identified CT image features between the two groups of patients. There was also no difference in the absolute and relative volume of infected regions in each lung segment.ConclusionWe documented the non-differentiating nature of initial chest CT image between COVID-19 and other viral pneumonia with suspected symptoms. Our results do not support CT findings replacing microbiological diagnosis as a critical criterion for COVID-19 diagnosis. Our findings may prompt re-evaluation of isolated patients without laboratory confirmation.

scholarly journals Chest CT Images for COVID-19: Radiologists and Computer-based detection

2020 ◽  
Author(s):  
Qingli Dou ◽  
Jiangping Liu ◽  
Wenwu Zhang ◽  
Yanan Gu ◽  
Wan-Ting Hsu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Analysis ◽  
Ct Images ◽  
Chest Ct ◽  
Image Analysis System ◽  
Ct Image ◽  
Chest Computed Tomography ◽  
Computer Based ◽  
Analysis System ◽  
Novel Coronavirus

ABSTRACTBackgroundCharacteristic chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) was added as a diagnostic criterion in the Chinese National COVID-19 management guideline. Whether the characteristic findings of Chest CT could differentiate confirmed COVID-19 cases from other positive nucleic acid test (NAT)-negative patients has not been rigorously evaluated.PurposeWe aim to test whether chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) can be differentiated by a radiologist or a computer-based CT image analysis system.MethodsWe conducted a retrospective case-control study that included 52 laboratory-confirmed COVID-19 patients and 80 non-COVID-19 viral pneumonia patients between 20 December, 2019 and 10 February, 2020. The chest CT images were evaluated by radiologists in a double blind fashion. A computer-based image analysis system (uAI system, Lianying Inc., Shanghai, China) detected the lesions in 18 lung segments defined by Boyden classification system and calculated the infected volume in each segment. The number and volume of lesions detected by radiologist and computer system was compared with Chi-square test or Mann-Whitney U test as appropriate.ResultsThe main CT manifestations of COVID-19 were multi-lobar/segmental peripheral ground-glass opacities and patchy air space infiltrates. The case and control groups were similar in demographics, comorbidity, and clinical manifestations. There was no significant difference in eight radiologist identified CT image features between the two groups of patients. There was also no difference in the absolute and relative volume of infected regions in each lung segment.ConclusionsWe documented the non-differentiating nature of initial chest CT image between COVID-19 and other viral pneumonia with suspected symptoms. Our results do not support CT findings replacing microbiological diagnosis as a critical criterion for COVID-19 diagnosis. Our findings may prompt re-evaluation of isolated patients without laboratory confirmation.

scholarly journals A Fully Automatic AI-based CT Image Analysis System for Accurate Detection, Diagnosis, and Quantitative Severity Evaluation of Pulmonary Tuberculosis

2021 ◽  
Author(s):  
Chenggong Yan ◽  
Lingfeng Wang ◽  
Jie Lin ◽  
Jun Xu ◽  
Tianjing Zhang ◽  
...  
Keyword(s):  
Image Analysis ◽  
Pulmonary Tuberculosis ◽  
Chest Ct ◽  
Ct Scans ◽  
Image Analysis System ◽  
Imaging Features ◽  
Ct Image ◽  
Pulmonary Tb ◽  
Ct Image Analysis ◽  
Analysis System

Abstract Background: Accurate and rapid diagnosis of pulmonary tuberculosis (TB) plays a crucial role in timely prevention and appropriate medical treatment to the disease. This study aims to develop and evaluate an artificial intelligence (AI)-based fully automated CT image analysis system for detection, diagnosis, and burden quantification of pulmonary TB.Methods: From December 2007 to September 2020, 892 chest CT scans from pathogen-confirmed TB patients were retrospectively included. A deep learning based cascading framework was connected to create a processing pipeline. To train and validate the model, 1921 lesions were manually labeled, classified by six categories of critical imaging features, and visually scored for the lesion involvement as the ground truth. “TB score” was calculated by the network-activation map to assess the disease burden quantitively. Independent test datasets from two additional hospitals and NIH TB Portal were used to validate externally the performance of the AI model.Results: CT scans from 526 participants (mean age, 48.5 years±16.5; 206 women) were analyzed. The lung lesion detection subsystem yielded a mean average precision of 0.68 on the validation cohort. In the independent datasets, the overall classification accuracy for six pulmonary critical imaging findings indicative of TB were 81.08%-91.05%. A moderate to strong correlation was demonstrated between the AI model quantified “TB score” and the radiologist-estimated CT score.Conclusion: This end-to-end AI system based on chest CT can achieve human-level diagnostic performance, and holds great potential for early management and medical resource optimization of patients with pulmonary TB in clinical practice.

A Computer‐based image analysis system for biocide screening

Biofouling ◽  
1996 ◽  
Vol 10 (1-3) ◽  
pp. 225-237 ◽  
Author(s):  
B Wigglesworth‐Cooksey ◽  
K E Cooksey
Keyword(s):  
Image Analysis ◽  
Image Analysis System ◽  
Computer Based ◽  
Analysis System

Application of Hybrid Vision Method for the Hot Aluminium Surface Inspection

2013 ◽  
Vol 199 ◽  
pp. 267-272 ◽  
Author(s):  
Tomasz Giesko ◽  
Piotr Garbacz
Keyword(s):  
Image Analysis ◽  
Temperature Distribution ◽  
Manufacturing Process ◽  
Surface Defects ◽  
Simultaneous Analysis ◽  
Surface Inspection ◽  
Image Analysis System ◽  
Computer Based ◽  
Aluminium Extrusion ◽  
Analysis System

The paper presents the possibilities of a hybrid vision method based on simultaneous analysis of infrared and vision images for surface inspection of hot aluminium in a manufacturing process. The system consists of a NIR/SWIR camera and a high resolution visual camera, and a computer based image analysis system. The simultaneous analysis of infrared and vision images will enable surface inspection for detecting defects in temperature range from 200°C to 600°C. Thermal images present temperature distribution on the surface, and contain information about the manufacturing process. The analysis of thermograms enables to find areas of temperature irregularity caused by increased friction loads, as well as areas of inhomogeneous emissivity caused by surface defects. Furthermore, information captured by the vision camera is used to detect surface defects. The software developed enables the overlaying of images. The proposed simultaneous thermovision and vision imaging can be applied in industry for in-line monitoring of aluminium extrusion processes.

Sequential topographical portrayal of myocardial blood flow

1989 ◽  
Vol 257 (2) ◽  
pp. H635-H642
Author(s):  
J. F. Richeson ◽  
R. C. Waag ◽  
D. Zwierzynski ◽  
E. A. Schenk
Keyword(s):  
Blood Flow ◽  
Image Analysis ◽  
Myocardial Blood Flow ◽  
Ischemic Injury ◽  
Radioactive Isotopes ◽  
Image Analysis System ◽  
Two Dimensional ◽  
Flow Maps ◽  
Computer Based ◽  
Analysis System

Methods to portray myocardial blood flow in a two-dimensional continuum are advantageous in that they allow blood flow history to be overlaid on histological or histochemical descriptions of the consequences of ischemia. We describe here autoradiographic methods that allow such portrayals at three separate times during the evolution of ischemic injury. A computer-based image-analysis system was used to derive such flow maps by taking advantage of the physical characteristics of radioactive isotopes.

Chemical Classification and Particle Sizing of Foundry Sands

1985 ◽  
Vol 43 ◽  
pp. 388-389
Author(s):  
D.S. DeMiglio
Keyword(s):  
Image Analysis ◽  
Energy Dispersive Spectrometer ◽  
Image Analysis System ◽  
Representative Sampling ◽  
Foundry Sands ◽  
Large Atomic Number ◽  
Backscattered Electron ◽  
Sand Particles ◽  
Analysis System ◽  
Reclamation System

Much progress has been made in recent years towards the development of closed-loop foundry sand reclamation systems. However, virtually all work to date has determined the effectiveness of these systems to remove surface clay and metal oxide scales by a qualitative inspection of a representative sampling of sand particles. In this investigation, particles from a series of foundry sands were sized and chemically classified by a Lemont image analysis system (which was interfaced with an SEM and an X-ray energy dispersive spectrometer) in order to statistically document the effectiveness of a reclamation system developed by The Pangborn Company - a subsidiary of SOHIO.The following samples were submitted: unreclaimed sand; calcined sand; calcined & mechanically scrubbed sand and unused sand. Prior to analysis, each sample was sprinkled onto a carbon mount and coated with an evaporated film of carbon. A backscattered electron photomicrograph of a field of scale-covered particles is shown in Figure 1. Due to a large atomic number difference between sand particles and the carbon mount, the backscattered electron signal was used for image analysis since it had a uniform contrast over the shape of each particle.

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