22 November 2018 Local complexity metrics to quantify the effect of anatomical noise on detectability of lung nodules in chest CT imaging
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Abstract
The purpose of this study is to (1) develop metrics to characterize the regional anatomical complexity of the lungs, and (2) relate these metrics with lung nodule detection in chest CT. A free-scrolling reader-study with virtually inserted nodules (13 radiologists × 157 total nodules = 2041 responses) is used to characterize human detection performance. Metrics of complexity based on the local density and orientation of distracting vasculature are developed for two-dimensional (2-D) and three-dimensional (3-D) considerations of the image volume. Assessed characteristics included the distribution of 2-D/3-D vessel structures of differing orientation (dubbed “2-D/3-D and dot-like/line-like distractor indices”), contiguity of inserted nodules with local vasculature, mean local gray-level surrounding each nodule, the proportion of lung voxels to total voxels in each section, and 3-D distance of each nodule from the trachea bifurcation. A generalized linear mixed-effects statistical model is used to determine the influence of each these metrics on nodule detectability. In order of decreasing effect size: 3-D line-like distractor index, 2-D line-like distractor index, 2-D dot-like distractor index, local mean gray-level, contiguity with 2-D dots, lung area, and contiguity with 3-D lines all significantly affect detectability (P  <  0.05). These data demonstrate that local lung complexity degrades detection of lung nodules.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2018/$25.00 © 2018 SPIE
Taylor Brunton Smith, Geoffrey D. Rubin, Justin Solomon, Brian Harrawood, Kingshuk Roy Choudhury, and Ehsan Samei "Local complexity metrics to quantify the effect of anatomical noise on detectability of lung nodules in chest CT imaging," Journal of Medical Imaging 5(4), 045502 (22 November 2018). https://doi.org/10.1117/1.JMI.5.4.045502
Received: 3 July 2018; Accepted: 23 October 2018; Published: 22 November 2018
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