Classification of lung disease in HRCT scans using integral geometry measures and functional data analysis

A framework for classification of chronic lung disease from high-resolution CT scans is presented. We use a set of features which measure the local morphology and topology of the 3D voxels within the lung parenchyma and apply functional data classification to the extracted features. We introduce the measures, Minkowski functionals, which derive from integral geometry and show results of classification on lungs containing various stages of chronic lung disease: emphysema, fibrosis and honey-combing. Once trained, the presented method is shown to be efficient and specific at characterising the distribution of disease in HRCT slices

Functional Imaging: CT and MRI

SYNOPSIS: Numerous imaging techniques permit evaluation of regional pulmonary function. Contrast-enhanced CT methods now allow assessment of vasculature and lung perfusion. Techniques using spirometric controlled MDCT allow for quantification of presence and distribution of parenchymal and airway pathology, Xenon gas can be employed to assess regional ventilation of the lungs and rapid bolus injections of iodinated contrast agent can provide quantitative measure of regional parenchymal perfusion. Advances in magnetic resonance imaging (MRI) of the lung include gadolinium-enhanced perfusion imaging and hyperpolarized helium imaging, which can allow imaging of pulmonary ventilation and .measurement of the size of emphysematous spaces

Pulmonary CT and MRI phenotypes that help explain chronic pulmonary obstruction disease pathophysiology and outcomes

Pulmonary x-ray computed tomographic (CT) and magnetic resonance imaging (MRI) research and development has been motivated, in part, by the quest to subphenotype common chronic lung diseases such as chronic obstructive pulmonary disease (COPD). For thoracic CT and MRI, the main COPD research tools, disease biomarkers are being validated that go beyond anatomy and structure to include pulmonary functional measurements such as regional ventilation, perfusion, and inflammation. In addition, there has also been a drive to improve spatial and contrast resolution while at the same time reducing or eliminating radiation exposure. Therefore, this review focuses on our evolving understanding of patient-relevant and clinically important COPD endpoints and how current and emerging MRI and CT tools and measurements may be exploited for their identification, quantification, and utilization. Since reviews of the imaging physics of pulmonary CT and MRI and reviews of other COPD imaging methods were previously published and well-summarized, we focus on the current clinical challenges in COPD and the potential of newly emerging MR and CT imaging measurements to address them. Here we summarize MRI and CT imaging methods and their clinical translation for generating reproducible and sensitive measurements of COPD related to pulmonary ventilation and perfusion as well as parenchyma morphology. The key clinical problems in COPD provide an important framework in which pulmonary imaging needs to rapidly move in order to address the staggering burden, costs, as well as the mortality and morbidity associated with COPD

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

This is what COPD looks like

Despite decades of research, and the growing healthcare and societal burden of chronic obstructive pulmonary disease (COPD), therapeutic COPD breakthroughs have not occurred. Sub-optimal COPD patient phenotyping, an incomplete understanding of COPD pathogenesis and a scarcity of sensitive tools that provide patient-relevant intermediate endpoints likely all play a role in the lack of new, efficacious COPD interventions. In other words, COPD patients are still diagnosed based on the presence of persistent airflow limitation measured using spirometry. Spirometry measurements reflect the global sum of all the different possible COPD pathologies and perhaps because of this, we lose sight of the different contributions of airway and parenchymal abnormalities. With recent advances in thoracic X-ray computed tomography (CT) and magnetic resonance imaging (MRI), lung structure and function abnormalities may be regionally identified and measured. These imaging endpoints may serve as biomarkers of COPD that can be used to better phenotype patients. Therefore, here we review novel CT and MRI measurements that help reveal COPD phenotypes and what COPD really \u27looks\u27 like, beyond spirometric indices. We discuss MR and CT imaging approaches for generating reproducible and sensitive measurements of COPD phenotypes related to pulmonary ventilation and perfusion as well as airway and parenchyma anatomical and morphological features. These measurements may provide a way to advance the development and testing of new COPD interventions and therapies

Chronic Obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation

Background Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast CT provides airway and parenchymal measurements but cannot be used to directly determine lung function. Purpose To develop, train, and test a CT texture analysis and machine-learning algorithm to predict lung ventilation heterogeneity in participants with COPD. Materials and Methods In this prospective study

Study Lung Tool: A Way to Understand HRTC Lung Parenchyma

Abstract-The purpose of the described system is to aid radiologists on their daily routine in the task of analyzing HRCT lung images and to contribute to a more accurate and fast diagnosis. We developed a framework -Study Lung Toolwith the objective of gather information from radiologists, in a systematic way. Using Study Lung Tool framework, the radiologist analyzes HRCT scans, outlines regions of typical pattern and characterizes the patterns. A database of typical patterns associated with common pulmonary diseases was created. The information gathered can be a valuable teaching tool to every one that intends to understand HRCT lung parenchyma. The proposed system discriminates between normal and abnormal patterns of lung parenchyma based on statistical texture analysis extracted from HRCT lung scans. An overall accuracy of 89,2%, a sensitivity of 92,7% and a specificity of 83,6% were achieved