Polypoid lesions of airways: early experience with computer-assisted detection by using virtual bronchoscopy and surface curvature.
OBJECTIVE
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.


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.


CONCLUSIONS
With this method, polypoid airway lesions can be detected automatically, although false-positive diagnoses present an important limitation.
Diagnostic Radiology Department, Warren Grant Magnuson Clinical Center, Bethesda, MD, USA.
R M
RM
Summers
W S
WS
Selbie
J D
JD
Malley
L M
LM
Pusanik
A J
AJ
Dwyer
N A
NA
Courcoutsakis
D J
DJ
Shaw
D E
DE
Kleiner
M C
MC
Sneller
C A
CA
Langford
S M
SM
Holland
J H
JH
Shelhamer
D000328:Adult; D000465:Algorithms; D001984:Bronchial Neoplasms / Q000175:diagnosis*; D020680:Bronchoscopes; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation*; D003937:Diagnosis, Differential; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D019047:Phantoms, Imaging; D011127:Polyps / Q000175:diagnosis*; D012680:Sensitivity and Specificity