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Proceedings Paper

Automated detection of presence of mucus foci in airway diseases: preliminary results
Author(s): Benjamin L. Odry; Atilla P. Kiraly; Carol L. Novak; David P. Naidich; Jane Ko; Myrna C. B. Godoy
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Paper Abstract

Chronic Obstructive Pulmonary Disease (COPD) is often characterized by partial or complete obstruction of airflow in the lungs. This can be due to airway wall thickening and retained secretions, resulting in foci of mucoid impactions. Although radiologists have proposed scoring systems to assess extent and severity of airway diseases from CT images, these scores are seldom used clinically due to impracticality. The high level of subjectivity from visual inspection and the sheer number of airways in the lungs mean that automation is critical in order to realize accurate scoring. In this work we assess the feasibility of including an automated mucus detection method in a clinical scoring system. Twenty high-resolution datasets of patients with mild to severe bronchiectasis were randomly selected, and used to test the ability of the computer to detect the presence or absence of mucus in each lobe (100 lobes in all). Two experienced radiologists independently scored the presence or absence of mucus in each lobe based on the visual assessment method recommended by Sheehan et al [1]. These results were compared with an automated method developed for mucus plug detection [2]. Results showed agreement between the two readers on 44% of the lobes for presence of mucus, 39% of lobes for absence of mucus, and discordant opinions on 17 lobes. For 61 lobes where 1 or both readers detected mucus, the computer sensitivity was 75.4%, the specificity was 69.2%, and the positive predictive value (PPV) was 79.3%. Six computer false positives were a-posteriori reviewed by the experts and reassessed as true positives, yielding results of 77.6% sensitivity, 81.8% for specificity, and 89.6% PPV.

Paper Details

Date Published: 28 February 2009
PDF: 8 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72603B (28 February 2009); doi: 10.1117/12.811923
Show Author Affiliations
Benjamin L. Odry, Siemens Corporate Research (United States)
Atilla P. Kiraly, Siemens Corporate Research (United States)
Carol L. Novak, Siemens Corporate Research (United States)
David P. Naidich, New York Univ. Medical Ctr. (United States)
Jane Ko, New York Univ. Medical Ctr. (United States)
Myrna C. B. Godoy, New York Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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