Literature DB >> 21723090

Computer-assisted detection of infectious lung diseases: a review.

Ulaş Bağcı1, Mike Bray, Jesus Caban, Jianhua Yao, Daniel J Mollura.   

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. Published by Elsevier Ltd.

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Year:  2011        PMID: 21723090      PMCID: PMC3207027          DOI: 10.1016/j.compmedimag.2011.06.002

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  101 in total

1.  Image feature analysis and computer-aided diagnosis in digital radiography: classification of normal and abnormal lungs with interstitial disease in chest images.

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Review 2.  Current status and future potential of computer-aided diagnosis in medical imaging.

Authors:  K Doi
Journal:  Br J Radiol       Date:  2005       Impact factor: 3.039

3.  Quantitation of emphysema by computed tomography using a "density mask" program and correlation with pulmonary function tests.

Authors:  M Kinsella; N L Müller; R T Abboud; N J Morrison; A DyBuncio
Journal:  Chest       Date:  1990-02       Impact factor: 9.410

4.  Best cases from the AFIP: fatal 2009 influenza A (H1N1) infection, complicated by acute respiratory distress syndrome and pulmonary interstitial emphysema.

Authors:  H Henry Guo; Robert T Sweeney; Donald Regula; Ann N Leung
Journal:  Radiographics       Date:  2010-01-12       Impact factor: 5.333

5.  On the convexity of ROC curves estimated from radiological test results.

Authors:  Lorenzo L Pesce; Charles E Metz; Kevin S Berbaum
Journal:  Acad Radiol       Date:  2010-08       Impact factor: 3.173

6.  Use of an artificial neural network to determine the diagnostic value of specific clinical and radiologic parameters in the diagnosis of interstitial lung disease on chest radiographs.

Authors:  Hiroyuki Abe; Kazuto Ashizawa; Shigehiko Katsuragawa; Heber MacMahon; Kunio Doi
Journal:  Acad Radiol       Date:  2002-01       Impact factor: 3.173

7.  Automatic detection of abnormalities in chest radiographs using local texture analysis.

Authors:  Bram van Ginneken; Shigehiko Katsuragawa; Bart M ter Haar Romeny; Kunio Doi; Max A Viergever
Journal:  IEEE Trans Med Imaging       Date:  2002-02       Impact factor: 10.048

8.  H1N1 influenza: initial chest radiographic findings in helping predict patient outcome.

Authors:  Galit Aviram; Amir Bar-Shai; Jacob Sosna; Ori Rogowski; Galia Rosen; Iuliana Weinstein; Arie Steinvil; Ofer Zimmerman
Journal:  Radiology       Date:  2010-04       Impact factor: 11.105

9.  Thoracic CT findings of novel influenza A (H1N1) infection in immunocompromised patients.

Authors:  Brett M Elicker; Brian S Schwartz; Catherine Liu; Eunice C Chen; Steve A Miller; Charles Y Chiu; W Richard Webb
Journal:  Emerg Radiol       Date:  2010-01-29

10.  Computer-aided detection of interstitial abnormalities in chest radiographs using a reference standard based on computed tomography.

Authors:  Yulia Arzhaeva; Mathias Prokop; David M J Tax; Pim A De Jong; Cornelia M Schaefer-Prokop; Bram van Ginneken
Journal:  Med Phys       Date:  2007-12       Impact factor: 4.071

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  25 in total

1.  Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks.

Authors:  Mingchen Gao; Ulas Bagci; Le Lu; Aaron Wu; Mario Buty; Hoo-Chang Shin; Holger Roth; Georgios Z Papadakis; Adrien Depeursinge; Ronald M Summers; Ziyue Xu; Daniel J Mollura
Journal:  Comput Methods Biomech Biomed Eng Imaging Vis       Date:  2016-06-06

2.  A generic approach to pathological lung segmentation.

Authors:  Awais Mansoor; Ulas Bagci; Ziyue Xu; Brent Foster; Kenneth N Olivier; Jason M Elinoff; Anthony F Suffredini; Jayaram K Udupa; Daniel J Mollura
Journal:  IEEE Trans Med Imaging       Date:  2014-07-08       Impact factor: 10.048

Review 3.  Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends.

Authors:  Awais Mansoor; Ulas Bagci; Brent Foster; Ziyue Xu; Georgios Z Papadakis; Les R Folio; Jayaram K Udupa; Daniel J Mollura
Journal:  Radiographics       Date:  2015 Jul-Aug       Impact factor: 5.333

4.  Shape and texture based novel features for automated juxtapleural nodule detection in lung CTs.

Authors:  Erdal Taşcı; Aybars Uğur
Journal:  J Med Syst       Date:  2015-03-03       Impact factor: 4.460

5.  A unified methodology based on sparse field level sets and boosting algorithms for false positives reduction in lung nodules detection.

Authors:  Soudeh Saien; Hamid Abrishami Moghaddam; Mohsen Fathian
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-08-09       Impact factor: 2.924

Review 6.  A review on segmentation of positron emission tomography images.

Authors:  Brent Foster; Ulas Bagci; Awais Mansoor; Ziyue Xu; Daniel J Mollura
Journal:  Comput Biol Med       Date:  2014-04-28       Impact factor: 4.589

7.  Automatic detection and quantification of tree-in-bud (TIB) opacities from CT scans.

Authors:  Ulas Bagci; Jianhua Yao; Albert Wu; Jesus Caban; Tara N Palmore; Anthony F Suffredini; Omer Aras; Daniel J Mollura
Journal:  IEEE Trans Biomed Eng       Date:  2012-03-14       Impact factor: 4.538

8.  AUTOMATIC QUANTIFICATION OF TREE-IN-BUD PATTERNS FROM CT SCANS.

Authors:  Ulas Bagci; Kirsten Miller-Jaster; Jianhua Yao; Albert Wu; Jesus Caban; Kenneth N Olivier; Omer Aras; Daniel J Mollura
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2012-12-31

9.  Computer-aided detection and quantification of cavitary tuberculosis from CT scans.

Authors:  Ziyue Xu; Ulas Bagci; Andre Kubler; Brian Luna; Sanjay Jain; William R Bishai; Daniel J Mollura
Journal:  Med Phys       Date:  2013-11       Impact factor: 4.071

10.  Segmentation of PET images for computer-aided functional quantification of tuberculosis in small animal models.

Authors:  Brent Foster; Ulas Bagci; Bappaditya Dey; Brian Luna; William Bishai; Sanjay Jain; Daniel J Mollura
Journal:  IEEE Trans Biomed Eng       Date:  2013-11-05       Impact factor: 4.538

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