A comparison of Atlas- and DL-based brain segmentation in hybrid PET/MR for regional SUV quantification

The analysis of abnormalities in various brain regions requires combining metabolic data from PET with anatomical segmentation from MR due to the relatively low resolution of PET images. In this study, we automatically segmented two representative brain areas from hybrid PET/MR scans of twelve PD and three MSA patients using both Atlas- and DL-based methods. We then compared the Standardized Uptake Values (SUVs) and accuracy of segmentation in the corresponding regions, with manual segmentation used as the ground truth for comparison. The results of this study indicate that the DL-based method produced superior segmentation accuracy compared to the Atlas-based method. However, there were no significant differences in the SUVmax and SUVmean across the different methods for the segmentation of Caudate and Putamen. Despite being more accurate for Caudate and Putamen segmentation, the DL-based method had little effect on the calculation of their SUVs in hybrid PET/MR scans, as compared to the widely used Atlas-based method.