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 . These results were compared with an automated method developed for mucus plug
detection . 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.