Journal
CLINICAL PHYSIOLOGY AND FUNCTIONAL IMAGING
Volume 43, Issue 2, Pages 71-77Publisher
WILEY
DOI: 10.1111/cpf.12793
Keywords
artificial intelligence; atherosclerosis; carotids; positron emission tomography
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This study compared a convolutional neural network (CNN) approach with manual segmentation of the common carotids. The results showed that the CNN-based segmentation method was much faster and provided results virtually identical to manual segmentation.
Background Carotid atherosclerosis is a major cause of stroke, traditionally diagnosed late. Positron emission tomography/computed tomography (PET/CT) with F-18-sodium fluoride (NaF) detects arterial wall micro-calcification long before macro-calcification becomes detectable by ultrasound, CT or magnetic resonance imaging. However, manual PET/CT processing is time-consuming and requires experience. We compared a convolutional neural network (CNN) approach with manual segmentation of the common carotids. Methods Segmentation in NaF-PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients were compared for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, and SUVtotal). SUVmean was the average of SUVmeans within the VOI, SUVmax the highest SUV in all voxels in the VOI, and SUVtotal the SUVmean multiplied by the Vol of the VOI. Intra and Interobserver variability with manual segmentation was examined in 25 randomly selected scans. Results Bias for Vol, SUVmean, SUVmax, and SUVtotal were 1.33 +/- 2.06, -0.01 +/- 0.05, 0.09 +/- 0.48, and 1.18 +/- 1.99 in the left and 1.89 +/- 1.5, -0.07 +/- 0.12, 0.05 +/- 0.47, and 1.61 +/- 1.47, respectively, in the right common carotid artery. Manual segmentation lasted typically 20 min versus 1 min with the CNN-based approach. Mean Vol deviation at repeat manual segmentation was 14% and 27% in left and right common carotids. Conclusions CNN-based segmentation was much faster and provided SUVmean values virtually identical to manually obtained ones, suggesting CNN-based analysis as a promising substitute of slow and cumbersome manual processing.
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