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Proceedings Paper

Development of local complexity metrics to quantify the effect of anatomical noise on detectability of lung nodules in chest CT imaging
Author(s): Justin Solomon; Geoffrey Rubin; Taylor Smith; Brian Harrawood; Kingshuk Roy Choudhury; Ehsan Samei
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Paper Abstract

The purpose of this study was to develop metrics of local anatomical complexity and compare them with detectability of lung nodules in CT. Data were drawn retrospectively from a published perception experiment in which detectability was assessed in cases enriched with virtual nodules (13 radiologists x 157 total nodules = 2041 responses). A local anatomical complexity metric called the distractor index was developed, defined as the Gaussian weighted proportion (i.e., average) of distracting local voxels (50 voxels in-plane, 5 slices). A distracting voxel was classified by thresholding image data that had been selectively filtered to enhance nodule-like features. The distractor index was measured for each nodule location in the nodule-free images. The local pixel standard deviation (STD) was also measured for each nodule. Other confounding factors of search fraction (proportion of lung voxels to total voxels in the given slice) and peripheral distance (defined as the 3D distance of the nodule from the trachea bifurcation) were measured. A generalized linear mixed-effects statistical model (no interaction terms, probit link function, random reader term) was fit to the data to determine the influence of each metric on detectability. In order of decreasing effect size: distractor index, STD, and search fraction all significantly affected detectability (P < 0.001). Distance to the trachea did not have a significant effect (P < 0.05). These data demonstrate that local lung complexity degrades detection of lung nodules and the distractor index could serve as a good surrogate metric to quantify anatomical complexity.

Paper Details

Date Published: 10 March 2017
PDF: 7 pages
Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101360X (10 March 2017); doi: 10.1117/12.2254044
Show Author Affiliations
Justin Solomon, Carl E. Ravin Advanced Imaging Labs. (United States)
Duke Univ. (United States)
Geoffrey Rubin, Duke Univ. (United States)
Taylor Smith, Carl E. Ravin Advanced Imaging Labs. (United States)
Duke Univ. (United States)
Brian Harrawood, Carl E. Ravin Advanced Imaging Labs. (United States)
Duke Univ. (United States)
Kingshuk Roy Choudhury, Carl E. Ravin Advanced Imaging Labs. (United States)
Duke Univ. (United States)
Ehsan Samei, Carl E. Ravin Advanced Imaging Labs. (United States)
Duke Univ. (United States)


Published in SPIE Proceedings Vol. 10136:
Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment
Matthew A. Kupinski; Robert M. Nishikawa, Editor(s)

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