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9 March 2010 A shape-dependent variability metric for evaluating panel segmentations with a case study on LIDC
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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.
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Stephen Siena, Olga Zinoveva, Daniela Raicu, Jacob Furst, and Samuel Armato III "A shape-dependent variability metric for evaluating panel segmentations with a case study on LIDC", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 762416 (9 March 2010);

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