Quantitative Assessment of Emphysema Severity in Histological Lung Analysis Articles uri icon

publication date

  • October 2015

start page

  • 2515

end page

  • 2529

issue

  • 10

volume

  • 43

International Standard Serial Number (ISSN)

  • 0090-6964

Electronic International Standard Serial Number (EISSN)

  • 1521-6047

abstract

  • Abstract: Emphysema 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 oftreatments. In this context, measures such as the mean linear intercept ð Þ Lm and the equivalent diameter ðdÞ 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.

keywords

  • chronic obstructive pulmonary disease (copd); animal models; parzen window stimation; lung segmentation; severity map; bayesian networks; biological organs; diseases; image segmentation; mammals; bayesian approaches; histological section; quantitative assessments; segmentation procedure; pulmonary diseases; animal experiment; animal model; animal tissue; article; bayesian learning; controlled study; disease association; disease severity assessment; equivalent diameter; histology; image analysis; lung emphysema; male; mean linear intercept; mouse; nonhuman; optimum window size; priority journal; quantitative analysis; respiratory tract parameters; severity index; tissue section; animal; disease model; lung; lung emphysema; pathology; severity of illness index; animals; disease models; mice; pulmonary emhysema; severity of illness index