PURPOSE: To test the application of a technique developed by the authors for the computer-assisted diagnosis of polypoid airway lesions from surface rendered virtual bronchoscopic reconstructions. MATERIALS AND METHODS: A computer algorithm was developed to detect polypoid airway lesions by means of segmentation of the bronchial surface with curvature classification. This method was tested with a bronchial phantom, five cadaveric lung specimens, and virtual bronchoscopic studies in 16 patients. RESULTS: For the patient studies, the sensitivity and specificity of the method were 47%-88% and 58%-89%, respectively, depending on the value of an adjustable parameter (the mean curvature threshold). The sensitivity increased (by 20% to 34%) when only lesions larger than 5 mm in diameter were considered. CONCLUSION: With this method, polypoid airway lesions can be detected automatically, although false-positive diagnoses present an important limitation.
PURPOSE: To test the application of a technique developed by the authors for the computer-assisted diagnosis of polypoid airway lesions from surface rendered virtual bronchoscopic reconstructions. MATERIALS AND METHODS: A computer algorithm was developed to detect polypoid airway lesions by means of segmentation of the bronchial surface with curvature classification. This method was tested with a bronchial phantom, five cadaveric lung specimens, and virtual bronchoscopic studies in 16 patients. RESULTS: For the patient studies, the sensitivity and specificity of the method were 47%-88% and 58%-89%, respectively, depending on the value of an adjustable parameter (the mean curvature threshold). The sensitivity increased (by 20% to 34%) when only lesions larger than 5 mm in diameter were considered. CONCLUSION: With this method, polypoid airway lesions can be detected automatically, although false-positive diagnoses present an important limitation.