Common carotid segmentation in 18F-sodium fluoride PET/CT scans: head-to-head comparison of artificial intelligence-based and manual method

Background
Carotid atherosclerosis is a major cause of stroke, traditionally diagnosed late. Positron emission tomography/computed tomography (PET/CT) with 18F-sodium fluoride (NaF) detects arterial wall micro-calcification long before macro-calcification becomes detectable by ultrasound, CT or MRI. 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, 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 volume of the VOI. Intra- and inter-observer 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 vs. 1 min with the CNN-based approach. Mean Vol deviation at repeat manual segmentation was 14 and 27 percent 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.