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The Lung Image Database Consortium (LIDC) Data Collection Process for Nodule Detection and Annotation

Authors :
Chris Piker
David Qing
Matthew S. Brown
John Freymann
Edwin J. R. van Beek
Charles R. Meyer
Anthony P. Reeves
Denise R. Aberle
Zaid J. Towfic
Geoffrey McLennan
David F. Yankelevitz
Laurence P. Clarke
Richie C. Pais
Ella A. Kazerooni
Michael F. McNitt-Gray
Gary E. Laderach
Peyton H. Bland
Barbara Y. Croft
Roger Engelmann
Heber MacMahon
Samuel G. Armato
Junfeng Guo
Source :
Mcnitt-Gray, M F, Armato, S G, Meyer, C R, Reeves, A P, Mclennan, G, Pais, R C, Freymann, J, Brown, M S, Engelmann, R M, Bland, P H, Laderach, G E, Piker, C, Guo, J, Towfic, Z, Qing, D P, Yankelevitz, D F, Aberle, D R, Van Beek, E J R, Macmahon, H, Kazerooni, E A, Croft, B Y & Clarke, L P 2007, ' The Lung Image Database Consortium (LIDC) Data Collection Process for Nodule Detection and Annotation ', Academic Radiology, vol. 14, no. 12, pp. 1464-1474 . https://doi.org/10.1016/j.acra.2007.07.021
Publication Year :
2007

Abstract

RATIONALE AND OBJECTIVES: The Lung Image Database Consortium (LIDC) is developing a publicly available database of thoracic computed tomography (CT) scans as a medical imaging research resource to promote the development of computer-aided detection or characterization of pulmonary nodules. To obtain the best estimate of the location and spatial extent of lung nodules, expert thoracic radiologists reviewed and annotated each scan. Because a consensus panel approach was neither feasible nor desirable, a unique two-phase, multicenter data collection process was developed to allow multiple radiologists at different centers to asynchronously review and annotate each CT scan. This data collection process was also intended to capture the variability among readers.MATERIALS AND METHODS: Four radiologists reviewed each scan using the following process. In the first or "blinded" phase, each radiologist reviewed the CT scan independently. In the second or "unblinded" review phase, results from all four blinded reviews were compiled and presented to each radiologist for a second review, allowing the radiologists to review their own annotations together with the annotations of the other radiologists. The results of each radiologist's unblinded review were compiled to form the final unblinded review. An XML-based message system was developed to communicate the results of each reading.RESULTS: This two-phase data collection process was designed, tested, and implemented across the LIDC. More than 500 CT scans have been read and annotated using this method by four expert readers; these scans either are currently publicly available at http://ncia.nci.nih.gov or will be in the near future.CONCLUSIONS: A unique data collection process was developed, tested, and implemented that allowed multiple readers at distributed sites to asynchronously review CT scans multiple times. This process captured the opinions of each reader regarding the location and spatial extent of lung nodules.

Details

Language :
English
Database :
OpenAIRE
Journal :
Mcnitt-Gray, M F, Armato, S G, Meyer, C R, Reeves, A P, Mclennan, G, Pais, R C, Freymann, J, Brown, M S, Engelmann, R M, Bland, P H, Laderach, G E, Piker, C, Guo, J, Towfic, Z, Qing, D P, Yankelevitz, D F, Aberle, D R, Van Beek, E J R, Macmahon, H, Kazerooni, E A, Croft, B Y & Clarke, L P 2007, ' The Lung Image Database Consortium (LIDC) Data Collection Process for Nodule Detection and Annotation ', Academic Radiology, vol. 14, no. 12, pp. 1464-1474 . https://doi.org/10.1016/j.acra.2007.07.021
Accession number :
edsair.doi.dedup.....b87b07dd62c36f9f993657ee869f3e0f
Full Text :
https://doi.org/10.1016/j.acra.2007.07.021