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

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

Quantitative Assessment of Emphysema Severity in Histological Lung Analysis

Published onlineEmphysema is a characteristic component of chronic obstructive pulmonary disease (COPD), which has been pointed out as one of the main causes of mortality for the next years. Animal models of emphysema are employed to study the evolution of this disease as well as the effect of treatments. In this context, measures such as the mean linear intercept (Lm) and the equivalent diameter (d) have been proposed to quantify the airspace enlargement associated with emphysematous lesions in histological sections. The parameter D2 , which relates the second and the third moments of the variable d , has recently shown to be a robust descriptor of airspace enlargement. However, the value of D2 does not provide a direct evaluation of emphysema severity. In our research, we suggest a Bayesian approach to map D2 onto a novel emphysema severity index (SI) reflecting the probability for a lung area to be emphysematous. Additionally, an image segmentation procedure was developed to compute the severity map of a lung section using the SI function. Severity maps corresponding to 54 lung sections from control mice, mice induced with mild emphysema and mice induced with severe emphysema were computed, revealing differences between the distribution of SI in the three groups. The proposed methodology could then assist in the quantification of emphysema severity in animal models of pulmonary disease.This work has been partly funded by the grants ‘‘MINECO DPI2012-38090-C03-02’’ and ‘‘TEC2013-48552-C2-1-R’’ from the Spanish Ministry of Economy and CompetitivenessPublicad

Segmentation of Lung Region in Computed Tomography (CT) Images

Segmentation of lung region in lung CT scan images is an important pre-processing technique prior automatic detection and classification of emphysema disease. Well segmented lung allows correct selection of region of interest (ROI) and thereby improve the abnormality detection and classification of the lung. In this work, a lung segmentation algorithm for CT images is proposed and evaluated. The proposed method is uses Gaussian smoothing filter followed by thresholding to create binary mask for the lung region. Formally, the binary mask will only select the lung region and zero the all the regions surrounding the lung area. The binary mask can be set to either separate the left and right lung or to show both lungs simultaneously. The algorithm is tested on a database of lung CT scan images of emphysema patients. The database contains top, middle and bottom sections of the lung. Evaluation of the algorithm using 39 middle section lung CT scan images give 15.38% correct segmentation of the left & right lung. With 39 top and 37 bottom section lung images, the algorithm give yy43% and 82.05% correct segmentation of lung. These results show the good potential of the proposed algorithm for segmentation of the lung region in CT images