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Proceedings Paper

Automated detection of mucus plugs within bronchial tree in MSCT images
Author(s): Benjamin L. Odry; Diran Guiliguian; Atilla P. Kiraly; Carol L. Novak; David P. Naidich; Jean-Francois Lerallut
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Paper Abstract

Pulmonary diseases characterized by chronic airway inflammation, such as Chronic Obstructive Pulmonary (COPD), result in abnormal bronchial wall thickening, lumen dilatation and mucus plugs. Multi-Slice Computed Tomography (MSCT) allows for assessment of these abnormalities, even in airways that are obliquely oriented to the scan plane. Chronic airway inflammation typically results in limitations of airflow, allowing for the accumulation of mucus, especially in the distal airways. In addition to obstructing airways, retained secretions make the airways prone to infection. Patients with chronic airway disease are clinically followed over time to assess disease progression and response to treatment. In this regard, the ability to obtain an automatic standardized method to rapidly and objectively assess the entire airway tree morphologically, including the extent of mucus plugging, would be of particular clinical value. We have developed a method to automatically detect the presence and location of mucus plugs within the peripheral airways. We first start with segmentation of the bronchial tree using a previously developed method. The skeleton-based tree structure is then computed and each terminal branch is individually extended using an adaptive threshold algorithm. We compute a local 2-dimensional model, based on airway luminal diameter and wall thickness. We then select a few points along the principal axis beyond the terminal branches, to extract 2D cross sections for correlation with a model of mucus plugging. Airway shape is validated with a correlation value, and the lumen distribution is analyzed and compared to the model. A high correlation indicates the presence of a mucus plug. We tested our method on 5 datasets containing a total of 40 foci of mucoid impaction. Preliminary results show sensitivity of 77.5% with a specificity of 98.2% and positive predictive value of 66%.

Paper Details

Date Published: 10 May 2007
PDF: 10 pages
Proc. SPIE 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images, 651110 (10 May 2007); doi: 10.1117/12.709783
Show Author Affiliations
Benjamin L. Odry, Siemens Corporate Research, Inc. (United States)
Univ. de Technologie de Compiegne (France)
Diran Guiliguian, Univ. de Technologie de Compiegne (France)
Atilla P. Kiraly, Siemens Corporate Research, Inc. (United States)
Carol L. Novak, Siemens Corporate Research, Inc. (United States)
David P. Naidich, New York Univ. Medical Ctr. (United States)
Jean-Francois Lerallut, Univ. de Technologie de Compiegne (France)


Published in SPIE Proceedings Vol. 6511:
Medical Imaging 2007: Physiology, Function, and Structure from Medical Images
Armando Manduca; Xiaoping P. Hu, Editor(s)

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