Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database

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dc.contributor.authorJacobs, Colin
dc.contributor.authorvan Rikxoort, Eva M.
dc.contributor.authorMurphy, Keelin
dc.contributor.authorProkop, Mathias
dc.contributor.authorSchaefer-Prokop, Cornelia M.
dc.contributor.authorvan Ginneken, Bram
dc.contributor.funderNederlandse Organisatie voor Wetenschappelijk Onderzoeken
dc.contributor.funderUniversität Bremenen
dc.contributor.funderMeVis Medical Solutions AGen
dc.date.accessioned2019-11-26T11:49:24Z
dc.date.available2019-11-26T11:49:24Z
dc.date.issued2015-10-06
dc.description.abstractObjectives: To benchmark the performance of state-of-the-art computer-aided detection (CAD) of pulmonary nodules using the largest publicly available annotated CT database (LIDC/IDRI), and to show that CAD finds lesions not identified by the LIDC’s four-fold double reading process. Methods: The LIDC/IDRI database contains 888 thoracic CT scans with a section thickness of 2.5 mm or lower. We report performance of two commercial and one academic CAD system. The influence of presence of contrast, section thickness, and reconstruction kernel on CAD performance was assessed. Four radiologists independently analyzed the false positive CAD marks of the best CAD system. Results: The updated commercial CAD system showed the best performance with a sensitivity of 82 % at an average of 3.1 false positive detections per scan. Forty-five false positive CAD marks were scored as nodules by all four radiologists in our study. Conclusions: On the largest publicly available reference database for lung nodule detection in chest CT, the updated commercial CAD system locates the vast majority of pulmonary nodules at a low false positive rate. Potential for CAD is substantiated by the fact that it identifies pulmonary nodules that were not marked during the extensive four-fold LIDC annotation process.en
dc.description.sponsorshipNetherlands Organisation for Scientific Research (NWO) (Project number 639.023.207)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationJacobs, C., van Rikxoort, E. M., Murphy, K., Prokop, M., Schaefer-Prokop, C. M. and van Ginneken, B. (2016) 'Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database', European Radiology, 26(7), pp. 2139-2147. doi: 10.1007/s00330-015-4030-7en
dc.identifier.doi10.1007/s00330-015-4030-7en
dc.identifier.eissn1432-1084
dc.identifier.endpage2147en
dc.identifier.issn0938-7994
dc.identifier.issued7en
dc.identifier.journaltitleEuropean Radiologyen
dc.identifier.startpage2139en
dc.identifier.urihttps://hdl.handle.net/10468/9248
dc.identifier.volume26en
dc.language.isoenen
dc.publisherSpringer Berlin Heidelbergen
dc.rights© 2915, The Author(s). This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectComputer-assisted diagnosisen
dc.subjectImage interpretationen
dc.subjectComputer-assisteden
dc.subjectLung canceren
dc.subjectSolitary pulmonary noduleen
dc.subjectLungen
dc.titleComputer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI databaseen
dc.typeArticle (peer-reviewed)en
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