Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images

A method for automatically quantifying emphysema regions using High-Resolution Computed Tomography (HRCT) scans of patients with chronic obstructive pulmonary disease (COPD) that does not require manually annotated scans for training is presented. HRCT scans of controls and of COPD patients with diverse disease severity are acquired at two different centers. Textural features from co-occurrence matrices and Gaussian filter banks are used to characterize the lung parenchyma in the scans. Two robust versions of multiple instance learning (MIL) classifiers, miSVM and MILES, are investigated. The classifiers are trained with the weak labels extracted from the forced expiratory volume in one minute (FEV$_1$) and diffusing capacity of the lungs for carbon monoxide (DLCO). At test time, the classifiers output a patient label indicating overall COPD diagnosis and local labels indicating the presence of emphysema. The classifier performance is compared with manual annotations by two radiologists, a classical density based method, and pulmonary function tests (PFTs). The miSVM classifier performed better than MILES on both patient and emphysema classification. The classifier has a stronger correlation with PFT than the density based method, the percentage of emphysema in the intersection of annotations from both radiologists, and the percentage of emphysema annotated by one of the radiologists. The correlation between the classifier and the PFT is only outperformed by the second radiologist. The method is therefore promising for facilitating assessment of emphysema and reducing inter-observer variability.Comment: Accepted at PLoS ON

Unsupervised and Weakly-Supervised Learning of Localized Texture Patterns of Lung Diseases on Computed Tomography

Computed tomography (CT) imaging enables in vivo assessment of lung parenchyma and several lung diseases. CT scans are key in particular for the diagnosis of 1) chronic obstructive pulmonary disease (COPD), which is the fourth leading cause of death worldwide, and largely overlaps with pulmonary emphysema; and 2) lung cancer, which is the first leading cause of cancer-related death, and manifests in its early stage with the presence of lung nodules. Most lung CT image analysis methods to-date have relied on supervised learning requiring manually annotated local regions of interest (ROIs), which are slow and labor-intensive to obtain. Machine learning models requiring less or no manual annotations are important for a sustainable development of computer-aided diagnosis (CAD) systems. This thesis focused on exploiting CT scans for lung disease characterization via two learning strategies: 1) fully unsupervised learning on a very large amount of unannotated image patches to discover novel lung texture patterns for pulmonary emphysema; and 2) weakly-supervised learning to generate voxel-level localization of lung nodules from CT whole-slice labels. In the first part of this thesis, we proposed an original unsupervised approach to learn emphysema-specific radiological texture patterns. We have designed dedicated spatial and texture features and a two-stage learning strategy incorporating clustering and graph partitioning. Learning was performed on a cohort of 2,922 high-resolution full-lung CT scans, which included a high prevalence of smokers and COPD subjects. Experiments lead to discovering 10 highly-reproducible spatially-informed lung texture patterns and 6 quantitative emphysema subtypes (QES). Our discovered QES were associated independently with distinct risk of symptoms, physiological changes, exacerbations and mortality. Genome-wide association studies identified loci associated with four subtypes. Then we designed a deep-learning approach, using unsupervised domain adaptation with adversarial training, to label the QES on cardiac CT scans, which included approximately 70% of the lung. Our proposed method accounted for the differences in CT image qualities, and enabled us to study the progression of QES on a cohort of 17,039 longitudinal cardiac and full-lung CT scans. Overall, the discovered QES provide novel emphysema sub-phenotyping that may facilitate future study of emphysema development, understanding the stages of COPD and the design of personalized therapies. In the second part of the thesis, we have designed a deep-learning method for lung nodule detection with weak labels, using classification convolutional neural networks (CNNs) with skip-connections to generate high-quality discriminative class activation maps, and a novel candidate screening framework to reduce the number of false positives. Given that the vast majority of annotated nodules are benign, we further exploited a data augmentation framework with a generative adversarial network (GAN) to address the issue of data imbalance for lung cancer prediction. Our weakly-supervised lung nodule detection on 1,000s CT scans achieved competitive performance compared to a fully-supervised method, while requiring 100 times less annotations. Our data augmentation framework enabled synthesizing nodules with high fidelity in specified categories, and is beneficial for predicting nodule malignancy scores and hence improving the accuracy / reliability of lung cancer screening

The association of plasma biomarkers with computed tomography-assessed emphysema phenotypes

Abstract Rationale Chronic obstructive pulmonary disease (COPD) is a phenotypically heterogeneous disease. In COPD, the presence of emphysema is associated with increased mortality and risk of lung cancer. High resolution computed tomography (HRCT) scans are useful in quantifying emphysema but are associated with radiation exposure and high incidence of false positive findings (i.e., nodules). Using a comprehensive biomarker panel, we sought to determine if there was a peripheral blood biomarker signature of emphysema. Methods 114 plasma biomarkers were measured using a custom assay in 588 individuals enrolled in the COPDGene study. Quantitative emphysema measurements included percent low lung attenuation (%LAA)≤ - 950 HU, ≤ -910 HU and mean lung attenuation at the 15th percentile on lung attenuation curve (LP15A). Multiple regression analysis was performed to determine plasma biomarkers associated with emphysema independent of covariates age, gender, smoking status, body mass index and FEV1. The findings were subsequently validated using baseline blood samples from a separate cohort of 388 subjects enrolled in the Treatment of Emphysema with a Selective Retinoid Agonist (TESRA) study. Results Regression analysis identified multiple biomarkers associated with CT-assessed emphysema in COPDGene, including advanced glycosylation end-products receptor (AGER or RAGE, p < 0.001), intercellular adhesion molecule 1 (ICAM, p < 0.001), and chemokine ligand 20 (CCL20, p < 0.001). Validation in the TESRA cohort revealed significant associations with RAGE, ICAM1, and CCL20 with radiologic emphysema (p < 0.001 after meta-analysis). Other biomarkers that were associated with emphysema include CDH1, CDH 13 and SERPINA7, but were not available for validation in the TESRA study. Receiver operating characteristics analysis demonstrated a benefit of adding a biomarker panel to clinical covariates for detecting emphysema, especially in those without severe airflow limitation (AUC 0.85). Conclusions Our findings, suggest that a panel of blood biomarkers including sRAGE, ICAM1 and CCL20 may serve as a useful surrogate measure of emphysema, and when combined with clinical covariates, may be useful clinically in predicting the presence of emphysema compared to just using covariates alone, especially in those with less severe COPD. Ultimately biomarkers may shed light on disease pathogenesis, providing targets for new treatments.http://deepblue.lib.umich.edu/bitstream/2027.42/134591/1/12931_2014_Article_127.pd

The association of plasma biomarkers with computed tomography-assessed emphysema phenotypes

Rationale: Chronic obstructive pulmonary disease (COPD) is a phenotypically heterogeneous disease. In COPD, the presence of emphysema is associated with increased mortality and risk of lung cancer. High resolution computed tomography (HRCT) scans are useful in quantifying emphysema but are associated with radiation exposure and high incidence of false positive findings (i.e., nodules). Using a comprehensive biomarker panel, we sought to determine if there was a peripheral blood biomarker signature of emphysema. Methods: 114 plasma biomarkers were measured using a custom assay in 588 individuals enrolled in the COPDGene study. Quantitative emphysema measurements included percent low lung attenuation (%LAA) ≤ −950 HU, ≤ − 910 HU and mean lung attenuation at the 15th percentile on lung attenuation curve (LP15A). Multiple regression analysis was performed to determine plasma biomarkers associated with emphysema independent of covariates age, gender, smoking status, body mass index and FEV1. The findings were subsequently validated using baseline blood samples from a separate cohort of 388 subjects enrolled in the Treatment of Emphysema with a Selective Retinoid Agonist (TESRA) study. Results: Regression analysis identified multiple biomarkers associated with CT-assessed emphysema in COPDGene, including advanced glycosylation end-products receptor (AGER or RAGE, p < 0.001), intercellular adhesion molecule 1 (ICAM, p < 0.001), and chemokine ligand 20 (CCL20, p < 0.001). Validation in the TESRA cohort revealed significant associations with RAGE, ICAM1, and CCL20 with radiologic emphysema (p < 0.001 after meta-analysis). Other biomarkers that were associated with emphysema include CDH1, CDH 13 and SERPINA7, but were not available for validation in the TESRA study. Receiver operating characteristics analysis demonstrated a benefit of adding a biomarker panel to clinical covariates for detecting emphysema, especially in those without severe airflow limitation (AUC 0.85). Conclusions: Our findings, suggest that a panel of blood biomarkers including sRAGE, ICAM1 and CCL20 may serve as a useful surrogate measure of emphysema, and when combined with clinical covariates, may be useful clinically in predicting the presence of emphysema compared to just using covariates alone, especially in those with less severe COPD. Ultimately biomarkers may shed light on disease pathogenesis, providing targets for new treatments. Electronic supplementary material The online version of this article (doi:10.1186/s12931-014-0127-9) contains supplementary material, which is available to authorized users

Transfer learning for multicenter classification of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a lung disease which can be quantified using chest computed tomography (CT) scans. Recent studies have shown that COPD can be automatically diagnosed using weakly supervised learning of intensity and texture distributions. However, up till now such classifiers have only been evaluated on scans from a single domain, and it is unclear whether they would generalize across domains, such as different scanners or scanning protocols. To address this problem, we investigate classification of COPD in a multi-center dataset with a total of 803 scans from three different centers, four different scanners, with heterogenous subject distributions. Our method is based on Gaussian texture features, and a weighted logistic classifier, which increases the weights of samples similar to the test data. We show that Gaussian texture features outperform intensity features previously used in multi-center classification tasks. We also show that a weighting strategy based on a classifier that is trained to discriminate between scans from different domains, can further improve the results. To encourage further research into transfer learning methods for classification of COPD, upon acceptance of the paper we will release two feature datasets used in this study on http://bigr.nl/research/projects/copdComment: Accepted at Journal of Biomedical and Health Informatic

Robust, Standardized Quantification of Pulmonary Emphysema in Low Dose CT Exams

RATIONALE AND OBJECTIVES: The aim of this study was to present and evaluate a fully automated system for emphysema quantification on low-dose computed tomographic images. The platform standardizes emphysema measurements against changes in the reconstruction algorithm and slice thickness. MATERIALS AND METHODS: Emphysema was quantified in 149 patients using a fully automatic, in-house developed software (the Robust Automatic On-Line Pulmonary Helper). The accuracy of the system was evaluated against commercial software, and its reproducibility was assessed using pairs of volume-corrected images taken 1 year apart. Furthermore, to standardize quantifications, the effect of changing the reconstruction parameters was modeled using a nonlinear fit, and the inverse of the model function was then applied to the data. The association between quantifications and pulmonary function testing was also evaluated. The accuracy of the in-house software compared to that of commercial software was measured using Spearman's rank correlation coefficient, the mean difference, and the intrasubject variability. Agreement between the methods was studied using Bland-Altman plots. To assess the reproducibility of the method, intraclass correlation coefficients and Bland-Altman plots were used. The statistical significance of the differences between the standardized data and the reference data (soft-tissue reconstruction algorithm B40f; slice thickness, 1 mm) was assessed using a paired two-sample t test. RESULTS: The accuracy of the method, measured as intrasubject variability, was 3.86 mL for pulmonary volume, 0.01% for emphysema index, and 0.39 Hounsfield units for mean lung density. Reproducibility, assessed using the intraclass correlation coefficient, was >0.95 for all measurements. The standardization method applied to compensate for variations in the reconstruction algorithm and slice thickness increased the intraclass correlation coefficients from 0.87 to 0.97 and from 0.99 to 1.00, respectively. The correlation of the standardized measurements with pulmonary function testing parameters was similar to that of the reference (for the emphysema index and the obstructive subgroup: forced expiratory volume in 1 second, -0.647% vs -0.615%; forced expiratory volume in 1 second/forced vital capacity, -0.672% vs -0.654%; and diffusing capacity for carbon monoxide adjusted for hemoglobin concentration, -0.438% vs -0.523%). CONCLUSIONS: The new emphysema quantification method presented in this report is accurate and reproducible and, thanks to its standardization method, robust to changes in the reconstruction parameters