Open AccessManual versus Automated Carotid Artery Plaque Component Segmentation in High and Lower Quality 3.0 Tesla MRI Scans
Author(s) -
Loek P. Smits,
Diederik F. van Wijk,
Raphaël Duivenvoorden,
Dongxiang Xu,
Chun Yuan,
Erik S.G. Stroes,
Aart J. Nederveen
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0164267
Subject(s) - medicine , reproducibility , intraclass correlation , segmentation , radiology , kappa , calcification , stenosis , magnetic resonance imaging , lumen (anatomy) , image quality , nuclear medicine , artificial intelligence , surgery , computer science , clinical psychology , linguistics , statistics , philosophy , mathematics , image (mathematics) , psychometrics
Purpose To study the interscan reproducibility of manual versus automated segmentation of carotid artery plaque components, and the agreement between both methods, in high and lower quality MRI scans. Methods 24 patients with 30–70% carotid artery stenosis were planned for 3T carotid MRI, followed by a rescan within 1 month. A multicontrast protocol (T1w,T2w, PDw and TOF sequences) was used. After co-registration and delineation of the lumen and outer wall, segmentation of plaque components (lipid-rich necrotic cores (LRNC) and calcifications) was performed both manually and automated. Scan quality was assessed using a visual quality scale. Results Agreement for the detection of LRNC ( Cohen’s kappa ( k) is 0.04) and calcification ( k = 0.41) between both manual and automated segmentation methods was poor. In the high-quality scans (visual quality score ≥ 3), the agreement between manual and automated segmentation increased to k = 0 .55 and k = 0.58 for, respectively, the detection of LRNC and calcification larger than 1 mm 2 . Both manual and automated analysis showed good interscan reproducibility for the quantification of LRNC (intraclass correlation coefficient (ICC) of 0.94 and 0.80 respectively) and calcified plaque area (ICC of 0.95 and 0.77, respectively). Conclusion Agreement between manual and automated segmentation of LRNC and calcifications was poor, despite a good interscan reproducibility of both methods. The agreement between both methods increased to moderate in high quality scans. These findings indicate that image quality is a critical determinant of the performance of both manual and automated segmentation of carotid artery plaque components.