Unsupervised Discovery of Spatially-Informed Lung Texture Patterns for Pulmonary Emphysema: The MESA COPD Study

Jie Yang; Elsa D Angelini; Pallavi P Balte; Eric A Hoffman; John H M Austin; Benjamin M Smith; Jingkuan Song; R Graham Barr; Andrew F Laine

doi:10.1007/978-3-319-66182-7_14

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Unsupervised Discovery of Spatially-Informed Lung Texture Patterns for Pulmonary Emphysema: The MESA COPD Study

Conference proceeding

Peer reviewed

Unsupervised Discovery of Spatially-Informed Lung Texture Patterns for Pulmonary Emphysema: The MESA COPD Study

Jie Yang, Elsa D Angelini, Pallavi P Balte, Eric A Hoffman, John H M Austin, Benjamin M Smith, Jingkuan Song, 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.10433, pp.116-124

09/2017

DOI: 10.1007/978-3-319-66182-7_14

PMCID: PMC5773120

PMID: 29354811

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http://doi.org/10.1007/978-3-319-66182-7_14View

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Abstract

Unsupervised discovery of pulmonary emphysema subtypes offers the potential for new definitions of emphysema on lung computed tomography (CT) that go beyond the standard subtypes identified on autopsy. Emphysema subtypes can be defined on CT as a variety of textures with certain spatial prevalence. However, most existing approaches for learning emphysema subtypes on CT are limited to texture features, which are sub-optimal due to the lack of spatial information. In this work, we exploit a standardized spatial mapping of the lung and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs). Our spatial mapping is demonstrated to be a powerful tool to study emphysema spatial locations over different populations. The discovered sLTPs are shown to have high reproducibility, ability to encode standard emphysema subtypes, and significant associations with clinical characteristics.

Algorithms

Lung - pathology

Reproducibility of Results

Humans

Sensitivity and Specificity

Pulmonary Emphysema - pathology

Poisson Distribution

Pulmonary Disease, Chronic Obstructive - diagnostic imaging

Tomography, X-Ray Computed

Lung - diagnostic imaging

Pulmonary Emphysema - diagnostic imaging

Details

Title: Subtitle: Unsupervised Discovery of Spatially-Informed Lung Texture Patterns for Pulmonary Emphysema: The MESA COPD Study
Creators: Jie Yang - Department of Biomedical Engineering, Columbia University, New York, NY, USA
Elsa D Angelini - ITMAT Data Science Group, NIHR Imperial BRC, Imperial College, London, UK
Pallavi P Balte - Department of Medicine, Columbia University Medical Center, New York, NY, USA
Eric A Hoffman - Department of Radiology, Medicine and Biomedical Engineering, University of Iowa, Iowa City, IA, USA
John H M Austin - Department of Radiology, Columbia University Medical Center, New York, NY, USA
Benjamin M Smith - Department of Medicine, McGill University Health Center, Montreal, QC, Canada
Jingkuan Song - Department of Biomedical Engineering, Columbia University, New York, NY, 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.10433, pp.116-124
DOI: 10.1007/978-3-319-66182-7_14
PMID: 29354811
PMCID: PMC5773120
eISBN: 3319661825; 9783319661827
ISSN: 0302-9743
eISSN: 1611-3349
Publisher: Germany
Grant note: N01HC95159 / NHLBI NIH HHS N01HC95169 / NHLBI NIH HHS N01 HC095159 / NHLBI NIH HHS UL1 RR025005 / NCRR NIH HHS UL1 RR024156 / NCRR NIH HHS R01 HL077612 / NHLBI NIH HHS R01 HL130506 / NHLBI NIH HHS R01 HL093081 / NHLBI NIH HHS N01 HC095169 / NHLBI NIH HHS R01 HL112986 / NHLBI NIH HHS R01 HL121270 / NHLBI NIH HHS RC1 HL100543 / NHLBI NIH HHS
Language: English
Date published: 09/2017
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
Record Identifier: 9984051560502771

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