ArticleJournal of medical imaging (Bellingham, Wash.)2018
Local complexity metrics to quantify the effect of anatomical noise on detectability of lung nodules in chest CT imaging.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Comparison of low-contrast detectability between uniform and anatomically realistic phantoms-influences on CT image quality assessment.European radiology · 2022Article
- Influence of background lung characteristics on nodule detection with computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Special Section Guest Editorial: Medical Image Perception and Observer Performance.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Local complexity metrics to quantify the effect of anatomical noise on detectability of lung nodules in chest CT imaging.Journal of medical imaging (Bellingham, Wash.) · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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 (
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