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Proceedings Paper

The Lung Image Database Consortium (LIDC): pulmonary nodule measurements, the variation, and the difference between different size metrics
Author(s): Anthony P. Reeves; Alberto M. Biancardi; Tatiyana V. Apanasovich; Charles R. Meyer; Heber MacMahon; Edwin J. R. van Beek; Ella A. Kazerooni; David Yankelevitz; Michael F. McNitt-Gray; Geoffrey McLennan; Samuel G. Armato; Denise R. Aberle; Claudia I. Henschke; Eric A. Hoffman; Barbara Y. Croft; Laurence P. Clarke
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Paper Abstract

Size is an important metric for pulmonary nodule characterization. Furthermore, it is an important parameter in measuring the performance of computer aided detection systems since they are always qualified with respect to a given size range of nodules. The first 120 whole-lung CT scans documented by the Lung Image Database Consortium using their protocol for nodule evaluation were used in this study. For documentation, each inspected lesion was reviewed independently by four expert radiologists and, when a lesion was considered to be a nodule larger than 3mm, the radiologist provided boundary markings in each image in which the nodule was contained. Three size metrics were considered: a uni-dimensional and a bi-dimensional measure on a single image slice and a volumetric measurement based on all the image slices. In this study we analyzed the boundary markings of these nodules in the context of these three size metrics to characterize the inter-radiologist variation and to examine the difference between these metrics. A data set of 63 nodules each having four observations was analyzed for inter-observer variation and an extended set of 252 nodules each having at least one observation was analyzed for the difference between the metrics. A very high inter-observer variation was observed for all these metrics and also a very large difference among the metrics was observed.

Paper Details

Date Published: 29 March 2007
PDF: 8 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65140J (29 March 2007); doi: 10.1117/12.713672
Show Author Affiliations
Anthony P. Reeves, Cornell Univ. (United States)
Alberto M. Biancardi, Cornell Univ. (United States)
Tatiyana V. Apanasovich, Cornell Univ. (United States)
Charles R. Meyer, The Univ. of Michigan (United States)
Heber MacMahon, The Univ. of Chicago (United States)
Edwin J. R. van Beek, Univ. of Iowa (United States)
Ella A. Kazerooni, The Univ. of Michigan (United States)
David Yankelevitz, Weill Medical College, Cornell Univ. (United States)
Michael F. McNitt-Gray, Univ. of California, Los Angeles (United States)
Geoffrey McLennan, Univ. of Iowa (United States)
Samuel G. Armato, The Univ. of Chicago (United States)
Denise R. Aberle, Univ. of California, Los Angeles (United States)
Claudia I. Henschke, Weill Medical College, Cornell Univ. (United States)
Eric A. Hoffman, Univ. of Iowa (United States)
Barbara Y. Croft, National Cancer Institute (United States)
Laurence P. Clarke, National Cancer Institute (United States)


Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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