Ilaria Bernardeschi, Presenter: Nothing to Disclose

Daniele Della Latta PhD, Abstract Co-Author: Nothing to Disclose

Giovanna Letizia Di Girolamo BEng, Abstract Co-Author: Nothing to Disclose

Vincenzo Positano, Abstract Co-Author: Nothing to Disclose

Michela Guadagni, Abstract Co-Author: Nothing to Disclose

Marta Patronelli, Abstract Co-Author: Nothing to Disclose

Chiara Orsini, Abstract Co-Author: Nothing to Disclose

Angelo Monteleone, Abstract Co-Author: Nothing to Disclose

Dante Chiappino MD, Abstract Co-Author: Nothing to Disclose

The developed method enables to accurately measure lumen diameter and wall thickness of the large and small airways. The reliability of the method makes it suitable for the evaluation of changes in patients with chronic disease.

Measurement of bronchial lumen diameter and wall thickness is essential in clinical practice for the characterization of morphological abnormalities and for monitoring changes in response to pharmacological treatment. In this work we developed a semi-automatic airways segmentation algorithm and an automatic analysis technique for the measurement of bronchial diameter and wall thickness of the large and the small airways.

Wall thickness and lumen diameter automatic measurement are not significantly different from the manual method of both the operators (P>0.17 and P>0.40 for diameter and wall thickness for first operator, P>0.09 and P>0.48 for second operator). The measurements are not significantly different from both intra and inter observer variability (P>0.06 and P>0.92 for diameter, P>0.29 and P>0.72 for wall thickness). The algorithm is totally reproducible. Despite the low operator interaction, the algorithm is able to segment bronchi beyond the twelfth generation, overcoming the main airways segmentation problems.

Images from 10 patients were acquired by a CT scanner. The advanced airways segmentation was performed through the application of a threshold based on patient gray-level histogram, first for the low variance and then for the high variance pattern. The only user action required is the selection of a seed belonging the trachea.A quantitative analysis method was applied on the obtained mask. The first step was center line extraction by skeletonization, followed by path extraction using maze algorithm, to identify bifurcation points and divide the bronchial tree in bronchial segments.The measurements were performed automatically on an image plane perpendicular to the bronchus axis, centered in the segment middle point, reconstructed by trilinear interpolation.For algorithm validation, 31 representative axial bronchus section were extracted by Vitrea VES 6.2 Software and manual measurements were done by two expert users.

Bernardeschi, I, Della Latta, D, Di Girolamo, G, Positano, V, Guadagni, M, Patronelli, M, Orsini, C, Monteleone, A, Chiappino, D, An Automated Method for Bronchial Tree Analysis from Trachea to the Small Airways: in Vivo Validation.  Radiological Society of North America 2012 Scientific Assembly and Annual Meeting, November 25 - November 30, 2012 ,Chicago IL. http://archive.rsna.org/2012/12029809.html