Computer-assisted detection of infectious lung diseases: A review

Ulaş Baĝci; Mike Bray; Jesus Caban; Jianhua Yao; Daniel J. Mollura

doi:10.1016/j.compmedimag.2011.06.002

Computer-assisted detection of infectious lung diseases: A review

Ulaş Baĝci^*, Mike Bray, Jesus Caban, Jianhua Yao, Daniel J. Mollura

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

68 Scopus citations

Abstract

Respiratory tract infections are a leading cause of death and disability worldwide. Although radiology serves as a primary diagnostic method for assessing respiratory tract infections, visual analysis of chest radiographs and computed tomography (CT) scans is restricted by low specificity for causal infectious organisms and a limited capacity to assess severity and predict patient outcomes. These limitations suggest that computer-assisted detection (CAD) could make a valuable contribution to the management of respiratory tract infections by assisting in the early recognition of pulmonary parenchymal lesions, providing quantitative measures of disease severity and assessing the response to therapy. In this paper, we review the most common radiographic and CT features of respiratory tract infections, discuss the challenges of defining and measuring these disorders with CAD, and propose some strategies to address these challenges.

Original language	English (US)
Pages (from-to)	72-84
Number of pages	13
Journal	Computerized Medical Imaging and Graphics
Volume	36
Issue number	1
DOIs	https://doi.org/10.1016/j.compmedimag.2011.06.002
State	Published - Jan 2012
Externally published	Yes

Keywords

Computer assisted detection
Feature extraction
Infectious diseases
Lung CT
Texture analysis
Tomography

ASJC Scopus subject areas

Radiological and Ultrasound Technology
Health Informatics
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.compmedimag.2011.06.002

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@article{3b27116a78d448deb1e5b841fa7fc256,

title = "Computer-assisted detection of infectious lung diseases: A review",

abstract = "Respiratory tract infections are a leading cause of death and disability worldwide. Although radiology serves as a primary diagnostic method for assessing respiratory tract infections, visual analysis of chest radiographs and computed tomography (CT) scans is restricted by low specificity for causal infectious organisms and a limited capacity to assess severity and predict patient outcomes. These limitations suggest that computer-assisted detection (CAD) could make a valuable contribution to the management of respiratory tract infections by assisting in the early recognition of pulmonary parenchymal lesions, providing quantitative measures of disease severity and assessing the response to therapy. In this paper, we review the most common radiographic and CT features of respiratory tract infections, discuss the challenges of defining and measuring these disorders with CAD, and propose some strategies to address these challenges.",

keywords = "Computer assisted detection, Feature extraction, Infectious diseases, Lung CT, Texture analysis, Tomography",

author = "Ula{\c s} Baĝci and Mike Bray and Jesus Caban and Jianhua Yao and Mollura, {Daniel J.}",

note = "Funding Information: This research is supported in part by the Imaging Sciences Training Program (ISTP), the Center for Infectious Disease Imaging (CIDI), and Intramural Research Program of the National Institute of Bio-imaging and Bioengineering at the National Institutes of Health. ",

year = "2012",

month = jan,

doi = "10.1016/j.compmedimag.2011.06.002",

language = "English (US)",

volume = "36",

pages = "72--84",

journal = "Computerized Medical Imaging and Graphics",

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T2 - A review

AU - Baĝci, Ulaş

AU - Bray, Mike

AU - Caban, Jesus

AU - Yao, Jianhua

AU - Mollura, Daniel J.

N1 - Funding Information: This research is supported in part by the Imaging Sciences Training Program (ISTP), the Center for Infectious Disease Imaging (CIDI), and Intramural Research Program of the National Institute of Bio-imaging and Bioengineering at the National Institutes of Health.

PY - 2012/1

Y1 - 2012/1

N2 - Respiratory tract infections are a leading cause of death and disability worldwide. Although radiology serves as a primary diagnostic method for assessing respiratory tract infections, visual analysis of chest radiographs and computed tomography (CT) scans is restricted by low specificity for causal infectious organisms and a limited capacity to assess severity and predict patient outcomes. These limitations suggest that computer-assisted detection (CAD) could make a valuable contribution to the management of respiratory tract infections by assisting in the early recognition of pulmonary parenchymal lesions, providing quantitative measures of disease severity and assessing the response to therapy. In this paper, we review the most common radiographic and CT features of respiratory tract infections, discuss the challenges of defining and measuring these disorders with CAD, and propose some strategies to address these challenges.

AB - Respiratory tract infections are a leading cause of death and disability worldwide. Although radiology serves as a primary diagnostic method for assessing respiratory tract infections, visual analysis of chest radiographs and computed tomography (CT) scans is restricted by low specificity for causal infectious organisms and a limited capacity to assess severity and predict patient outcomes. These limitations suggest that computer-assisted detection (CAD) could make a valuable contribution to the management of respiratory tract infections by assisting in the early recognition of pulmonary parenchymal lesions, providing quantitative measures of disease severity and assessing the response to therapy. In this paper, we review the most common radiographic and CT features of respiratory tract infections, discuss the challenges of defining and measuring these disorders with CAD, and propose some strategies to address these challenges.

KW - Computer assisted detection

KW - Feature extraction

KW - Infectious diseases

KW - Lung CT

KW - Texture analysis

KW - Tomography

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DO - 10.1016/j.compmedimag.2011.06.002

M3 - Review article

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AN - SCOPUS:83555177603

SN - 0895-6111

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JO - Computerized Medical Imaging and Graphics

JF - Computerized Medical Imaging and Graphics

IS - 1

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Computer-assisted detection of infectious lung diseases: A review

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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