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Proceedings Paper

A shape-dependent variability metric for evaluating panel segmentations with a case study on LIDC
Author(s): Stephen Siena; Olga Zinoveva; Daniela Raicu; Jacob Furst; Samuel Armato
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Paper Abstract

The segmentation of medical images is challenging because a ground truth is often not available. Computer-Aided Detection (CAD) systems are dependent on ground truth as a means of comparison; however, in many cases the ground truth is derived from only experts' opinions. When the experts disagree, it becomes impossible to discern one ground truth. In this paper, we propose an algorithm to measure the disagreement among radiologist's delineated boundaries. The algorithm accounts for both the overlap and shape of the boundaries in determining the variability of a panel segmentation. After calculating the variability of 3788 thoracic computed tomography (CT) slices in the Lung Image Database Consortium (LIDC), we found that the radiologists have a high consensus in a majority of lung nodule segmentations. However, our algorithm identified a number of segmentations that the radiologists significantly disagreed on. Our proposed method of measuring disagreement can assist others in determining the reliability of panel segmentations. We also demonstrate that it is superior to simply using overlap, which is currently one of the most common ways of measuring segmentation agreement. The variability metric presented has applications to panel segmentations, and also has potential uses in CAD systems.

Paper Details

Date Published: 9 March 2010
PDF: 8 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 762416 (9 March 2010); doi: 10.1117/12.844639
Show Author Affiliations
Stephen Siena, Univ. of Notre Dame (United States)
Olga Zinoveva, Harvard Univ. (United States)
Daniela Raicu, DePaul Univ. (United States)
Jacob Furst, DePaul Univ. (United States)
Samuel Armato, The Univ. of Chicago (United States)

Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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