Lung micronodules: automated method for detection at thin-section CT--initial experience.
An automated system was developed for detecting lung micronodules on thin-section computed tomographic images and was applied to data from 15 subjects with 77 lung nodules. The automated system, without user interaction, achieved a sensitivity of 100% for nodules (>3 mm in diameter) and 70% for micronodules (<or=3 mm). With the same images, a radiologist detected nodules and micronodules with sensitivities of 91% and 51%, respectively, without system input. With assistance from the automated system, these sensitivities increased to 95% and 74%, respectively. Preliminary results indicate that the automated system considerably improved the radiologist's performance in micronodule detection.
Brown
Matthew S
MS
Department of Radiology, David Geffen School of Medicine at UCLA, 10833 Le Conte Ave, Los Angeles, CA 90095-1721, USA. mbrown@mednet.ucla.edu
Goldin
Jonathan G
JG
Suh
Robert D
RD
McNitt-Gray
Michael F
MF
Sayre
James W
JW
Aberle
Denise R
DR
D000368:Aged; D000369:Aged, 80 and over; D001331:Automation; D005260:Female; D006801:Humans; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D008297:Male; D008875:Middle Aged; D012680:Sensitivity and Specificity; D012985:Software Design; D014057:Tomography, X-Ray Computed / Q000379:methods*