| Literature DB >> 12511699 |
Matthew S Brown1, Jonathan G Goldin, Robert D Suh, Michael F McNitt-Gray, James W Sayre, Denise R Aberle.
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. Copyright RSNA, 2002Mesh:
Year: 2003 PMID: 12511699 DOI: 10.1148/radiol.2261011708
Source DB: PubMed Journal: Radiology ISSN: 0033-8419 Impact factor: 11.105