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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.