title: Lung micronodules: automated method for detection at thin-section CT--initial experience.
abstract: 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.
authors.affiliation: Department of Radiology, David Geffen School of Medicine at UCLA, 10833 Le Conte Ave, Los Angeles, CA 90095-1721, USA. mbrown@mednet.ucla.edu
authors.forename: Matthew S
authors.initials: MS
authors.lastname: Brown
authors.forename: Jonathan G
authors.initials: JG
authors.lastname: Goldin
authors.forename: Robert D
authors.initials: RD
authors.lastname: Suh
authors.forename: Michael F
authors.initials: MF
authors.lastname: McNitt-Gray
authors.forename: James W
authors.initials: JW
authors.lastname: Sayre
authors.forename: Denise R
authors.initials: DR
authors.lastname: Aberle
mesh_terms: 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*