Computer-aided recognition of emphysema on digital chest radiography

Miniati, Massimo; Coppini, Giuseppe; Monti, Simonetta; Bottai, Matteo; Paterni, Marco; Ferdeghini, E.M.

doi:10.1016/j.ejrad.2010.08.021

Cited by 11 publications

(12 citation statements)

References 14 publications

(21 reference statements)

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“…Recently, a neural network-based, computer-aided method was introduced that allows online recognition of emphysema on digital chest radiographs [22]. This method, validated against CT, might be used advantageously by clinicians because it is simple and inexpensive.…”

Section: Discussionmentioning

confidence: 99%

Effects of emphysema on oxygen uptake during maximal exercise in COPD

et al. 2011

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The objective of the study is to assess the effects of emphysema on peak oxygen uptake ( _ VO 2peak ) during a cardiopulmonary exercise test in patients with chronic obstructive pulmonary disease (COPD). We measured _ VO 2peak and oxygen pulse in 80 patients with stable COPD exercising maximally. Oxygen saturation was measured by pulse oximetry (SpO 2 ), and the ventilatory response assessed by the ratio of tidal volume (V T ) at peak to slow vital capacity (SVC) at baseline, and by the percent increase of peak V T over baseline. Computed tomography imaging (CT scan) served as the reference diagnostic standard for emphysema. Based on the panel-grading (PG) method, emphysema was rated absent or mild (PG B 30, n = 54), or moderate to severe (PG [ 30, n = 26).Multiple quantile regression was applied to estimate the effects of PG [ 30 on _ VO 2peak . At peak exercise, the patients with PG [ 30 had significantly lower _ VO 2 , oxygen pulse and SpO 2 , and featured a blunted ventilatory response with respect to those with PG B 30 (p \ 0.001). With multiple quantile regression, the effects of PG [ 30 on _ VO 2Peak were only partially explained by the degree of lung hyperinflation, a substantial component being imputable to impairment of lung diffusing capacity. In conclusion, chronic obstructive pulmonary disease patients with moderate to severe emphysema feature significantly lower exercise tolerance than those with no or mild emphysema. Our findings underscore the need of tailoring therapeutic interventions for COPD to the predominant clinical phenotype to improve exercise capacity.

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Section: Discussionmentioning

confidence: 99%

Effects of emphysema on oxygen uptake during maximal exercise in COPD

et al. 2011

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“…It is worth noting that the availability of lung boundary in postero-anterior and lateral views may be usefully exploited to compute additional parameters such as the radiographic total lung capacity [31]. As to shape description and classification, the described system exhibits improved recognition capabilities in comparison with previous works [8,10]. In particular, the adopted shape-descriptor was able to successfully cope with alterations of lung silhouette not related to the presence of emphysema, such as skeletal distortions.…”

Section: Discussionmentioning

confidence: 97%

“…A polyline shape description applied to standard two-sided chest radiographs, yielded an accuracy of about 90.3%, a sensitivity of 88.3%, and a specificity 90.7%. More recently those findings were confirmed against CT imaging [10]. On these bases, automatic procedures to assist emphysema diagnosis in clinical routine could be designed according to a simple computational framework including segmentation of lung silhouettes in two-view radiography and analysis of their shape.…”

Section: Introductionmentioning

confidence: 91%

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A computer-aided diagnosis approach for emphysema recognition in chest radiography

Coppini

Miniati

Monti

et al. 2013

Medical Engineering & Physics

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a b s t r a c tThe purpose of this work is twofold: (i) to develop a CAD system for the assessment of emphysema by digital chest radiography and (ii) to test it against CT imaging. The system is based on the analysis of the shape of lung silhouette as imaged in standard chest examination. Postero-anterior and lateral views are processed to extract the contours of the lung fields automatically. Subsequently, the shape of lung silhouettes is described by polyline approximation and the computed feature-set processed by a neural network to estimate the probability of emphysema.Images of radiographic studies from 225 patients were collected and properly annotated to build an experimental dataset named EMPH. Each patient had undergone a standard two-views chest radiography and CT for diagnostic purposes. In addition, the images (247) from JSRT dataset were used to evaluate lung segmentation in postero-anterior view.System performances were assessed by: (i) analyzing the quality of the automatic segmentation of the lung silhouette against manual tracing and (ii) measuring the capabilities of emphysema recognition. As to step i, on JSRT dataset, we obtained overlap percentage (˝) 92.7 ± 3.3%, Dice Similarity Coefficient (DSC) 95.5 ± 3.7% and average contour distance (ACD) 1.73 ± 0.87 mm. On EMPH dataset we had ˝ = 93.1 ± 2.9%, DSC = 96.1 ± 3.5% and ACD = 1.62 ± 0.92 mm, for the postero-anterior view, while we had ˝ = 94.5 ± 4.6%, DSC = 91.0 ± 6.3% and ACD = 2.22 ± 0.86 mm, for the lateral view. As to step ii, accuracy of emphysema recognition was 95.4%, with sensitivity and specificity 94.5% and 96.1% respectively. According to experimental results our system allows reliable and inexpensive recognition of emphysema on digital chest radiography.

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“…There is an inherent subjectivity on visual evaluation of this disease. 21 Thus, for a given lung patch, the diagnosis may differ between experts. Binary classification between normal and emphysematous categories does not accurately characterize the patch under evaluation.…”

Section: Discussionmentioning

confidence: 99%

A probabilistic model of emphysema based on granulometry analysis

et al. 2013

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Emphysema is associated with the destruction of lung parenchyma, resulting in abnormal enlargement of airspaces. Accurate quantification of emphysema is required for a better understanding of the disease as well as for the assessment of drugs and treatments. In the present study, a novel method for emphysema characterization from histological lung images is proposed. Elastase-induced mice were used to simulate the effect of emphysema on the lungs. A database composed of 50 normal and 50 emphysematous lung patches of size 512 x 512 pixels was used in our experiments. The purpose is to automatically identify those patches containing emphysematous tissue. The proposed approach is based on the use of granulometry analysis, which provides the pattern spectrum describing the distribution of airspaces in the lung region under evaluation. The profile of the spectrum was summarized by a set of statistical features. A logistic regression model was then used to estimate the probability for a patch to be emphysematous from this feature set. An accuracy of 87% was achieved by our method in the classification between normal and emphysematous samples. This result shows the utility of our granulometry-based method to quantify the lesions due to emphysema.

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Computer-aided recognition of emphysema on digital chest radiography

Cited by 11 publications

References 14 publications

Effects of emphysema on oxygen uptake during maximal exercise in COPD

Effects of emphysema on oxygen uptake during maximal exercise in COPD

A computer-aided diagnosis approach for emphysema recognition in chest radiography

A probabilistic model of emphysema based on granulometry analysis

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