DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bae K.T. | ko |
dc.contributor.author | Kim J.-S. | ko |
dc.contributor.author | Na Y.-H. | ko |
dc.contributor.author | Kim K.G. | ko |
dc.contributor.author | Kim J.-H. | ko |
dc.date.accessioned | 2013-03-06T16:41:33Z | - |
dc.date.available | 2013-03-06T16:41:33Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2005 | - |
dc.identifier.citation | RADIOLOGY, v.236, no.1, pp.286 - 294 | - |
dc.identifier.issn | 0033-8419 | - |
dc.identifier.uri | http://hdl.handle.net/10203/87633 | - |
dc.description.abstract | Institutional review board approval was obtained. Informed patient consent was not required for this retrospective study, which involved review of previously obtained image data. Patient confidentiality was protected; the study was compliant with the Health Insurance Portability and Accountability Act. An automated pulmonary nodule detection program that takes advantage of three-dimensional volumetric data was developed and tested with multi-detector row computed tomographic (0) images from 20 patients (13 men, seven women; age range, 40-75 years) with pulmonary nodules. A total of 164 nodules 3 mm in diameter and larger were detected by two radiologists in consensus and were used as a reference standard to evaluate the computer-aided detection (CAD) program. The CAD algorithm was structured to process nodules that were categorized into three types: isolated, juxtapleural, and juxtavascular. Overall sensitivity for nodule detection with the CAD program was 95.1% (156 of 164 nodules). The sensitivity according to nodule size was 91.2% (52 of 57 nodules) for nodules 3 mm to less than 5 mm and 97.2% (104 of 107 nodules) for nodules 5 mm and larger. The number of false-positive detections per patient was 6.9 for false nodule structures 3 mm and larger and 4.0 for false nodule structures 5 mm and larger. ((c)) RSNA, 2005. | - |
dc.language | English | - |
dc.publisher | RADIOLOGICAL SOC NORTH AMERICA | - |
dc.subject | COMPUTER-AIDED DIAGNOSIS | - |
dc.subject | LUNG-CANCER | - |
dc.subject | TOMOGRAPHY IMAGES | - |
dc.subject | SPIRAL CT | - |
dc.subject | EXPERIENCE | - |
dc.subject | READINGS | - |
dc.subject | SYSTEM | - |
dc.subject | TERMS | - |
dc.title | Pulmonary nodules: Automated detection on CT images with morphologic matching algorithm preliminary results | - |
dc.type | Article | - |
dc.identifier.wosid | 000229905300037 | - |
dc.identifier.scopusid | 2-s2.0-20744460878 | - |
dc.type.rims | ART | - |
dc.citation.volume | 236 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 286 | - |
dc.citation.endingpage | 294 | - |
dc.citation.publicationname | RADIOLOGY | - |
dc.identifier.doi | 10.1148/radiol.2361041286 | - |
dc.contributor.localauthor | Kim J.-S. | - |
dc.contributor.nonIdAuthor | Bae K.T. | - |
dc.contributor.nonIdAuthor | Na Y.-H. | - |
dc.contributor.nonIdAuthor | Kim K.G. | - |
dc.contributor.nonIdAuthor | Kim J.-H. | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordPlus | COMPUTER-AIDED DIAGNOSIS | - |
dc.subject.keywordPlus | LUNG-CANCER | - |
dc.subject.keywordPlus | TOMOGRAPHY IMAGES | - |
dc.subject.keywordPlus | SPIRAL CT | - |
dc.subject.keywordPlus | EXPERIENCE | - |
dc.subject.keywordPlus | READINGS | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordPlus | TERMS | - |
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