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Proceedings Paper

Comparison of computer versus manual determination of pulmonary nodule volumes in CT scans
Author(s): Alberto M. Biancardi; Anthony P. Reeves; Artit C. Jirapatnakul; Tatiyana Apanasovitch; David Yankelevitz; Claudia I. Henschke
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Paper Abstract

Accurate nodule volume estimation is necessary in order to estimate the clinically relevant growth rate or change in size over time. An automated nodule volume-measuring algorithm was applied to a set of pulmonary nodules that were documented by the Lung Image Database Consortium (LIDC). The LIDC process model specifies that each scan is assessed by four experienced thoracic radiologists and that boundaries are to be marked around the visible extent of the nodules for nodules 3 mm and larger. Nodules were selected from the LIDC database with the following inclusion criteria: (a) they must have a solid component on a minimum of three CT image slices and (b) they must be marked by all four LIDC radiologists. A total of 113 nodules met the selection criterion with diameters ranging from 3.59 mm to 32.68 mm (mean 9.37 mm, median 7.67 mm). The centroid of each marked nodule was used as the seed point for the automated algorithm. 95 nodules (84.1%) were correctly segmented, but one was considered not meeting the first selection criterion by the automated method; for the remaining ones, eight (7.1%) were structurally too complex or extensively attached and 10 (8.8%) were considered not properly segmented after a simple visual inspection by a radiologist. Since the LIDC specifications, as aforementioned, instruct radiologists to include both solid and sub-solid parts, the automated method core capability of segmenting solid tissues was augmented to take into account also the nodule sub-solid parts. We ranked the distances of the automated method estimates and the radiologist-based estimates from the median of the radiologist-based values. The automated method was in 76.6% of the cases closer to the median than at least one of the values derived from the manual markings, which is a sign of a very good agreement with the radiologists' markings.

Paper Details

Date Published: 1 April 2008
PDF: 8 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691510 (1 April 2008); doi: 10.1117/12.771071
Show Author Affiliations
Alberto M. Biancardi, Cornell Univ. (United States)
Anthony P. Reeves, Cornell Univ. (United States)
Artit C. Jirapatnakul, Cornell Univ. (United States)
Tatiyana Apanasovitch, Cornell Univ. (United States)
David Yankelevitz, Weill Medical College of Cornell Univ. (United States)
Claudia I. Henschke, Weill Medical College of Cornell Univ. (United States)

Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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