Emphysema Quantification on Cardiac CT Scans Using Hidden Markov Measure Field Model: The MESA Lung Study

Jie Yang; Elsa D Angelini; Pallavi P Balte; Eric A Hoffman; Colin O Wu; Bharath A Venkatesh; R Graham Barr; Andrew F Laine

doi:10.1007/978-3-319-46723-8_72

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Emphysema Quantification on Cardiac CT Scans Using Hidden Markov Measure Field Model: The MESA Lung Study

Conference proceeding

Peer reviewed

Emphysema Quantification on Cardiac CT Scans Using Hidden Markov Measure Field Model: The MESA Lung Study

Jie Yang, Elsa D Angelini, Pallavi P Balte, Eric A Hoffman, Colin O Wu, Bharath A Venkatesh, R Graham Barr and Andrew F Laine

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, Vol.9901, pp.624-631

10/2016

DOI: 10.1007/978-3-319-46723-8_72

PMCID: PMC5569897

PMID: 28845485

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http://doi.org/10.1007/978-3-319-46723-8_72View

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Abstract

Cardiac computed tomography (CT) scans include approximately 2/3 of the lung and can be obtained with low radiation exposure. Large cohorts of population-based research studies reported high correlations of emphysema quantification between full-lung (FL) and cardiac CT scans, using thresholding-based measurements. This work extends a hidden Markov measure field (HMMF) model-based segmentation method for automated emphysema quantification on cardiac CT scans. We show that the HMMF-based method, when compared with several types of thresholding, provides more reproducible emphysema segmentation on repeated cardiac scans, and more consistent measurements between longitudinal cardiac and FL scans from a diverse pool of scanner types and thousands of subjects with ten thousands of scans.

Algorithms

Markov Chains

Reproducibility of Results

Humans

Sensitivity and Specificity

Tomography, X-Ray Computed

Lung - diagnostic imaging

Pulmonary Emphysema - diagnostic imaging

Heart - diagnostic imaging

Details

Title: Subtitle: Emphysema Quantification on Cardiac CT Scans Using Hidden Markov Measure Field Model: The MESA Lung Study
Creators: Jie Yang - Department of Biomedical Engineering, Columbia University, New York, NY, USA
Elsa D Angelini - Department of Biomedical Engineering, Columbia University, New York, NY, USA
Pallavi P Balte - Department of Medicine, Columbia University Medical Center, New York, NY, USA
Eric A Hoffman - Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
Colin O Wu - Office of Biostatistics Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
Bharath A Venkatesh - Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
R Graham Barr - Department of Epidemiology, Columbia University Medical Center, New York, NY, USA
Andrew F Laine - Department of Biomedical Engineering, Columbia University, New York, NY, USA
Resource Type: Conference proceeding
Publication Details: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, Vol.9901, pp.624-631
DOI: 10.1007/978-3-319-46723-8_72
PMID: 28845485
PMCID: PMC5569897
eISBN: 3319467239; 9783319467238
ISSN: 0302-9743
eISSN: 1611-3349
Publisher: Germany
Grant note: P30 ES005605 / NIEHS NIH HHS N01 HC095159 / NHLBI NIH HHS UL1 RR025005 / NCRR NIH HHS UL1 RR024156 / NCRR NIH HHS R01 HL077612 / NHLBI NIH HHS R01 HL093081 / NHLBI NIH HHS N01 HC095169 / NHLBI NIH HHS R01 HL121270 / NHLBI NIH HHS R01 HL112986 / NHLBI NIH HHS RC1 HL100543 / NHLBI NIH HHS
Language: English
Date published: 10/2016
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
Record Identifier: 9984051772002771

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