Segmentation of Dynamic Total-Body [¹⁸F]-FDG PET Images Using Unsupervised Clustering.

ABSTRACT: Clustering time activity curves of PET images have been used to separate clinically relevant areas of the brain or tumours. However, PET image segmentation in multiorgan level is much less studied due to the available total-body data being limited to animal studies. Now, the new PET scanners providing the opportunity to acquire total-body PET scans also from humans are becoming more common, which opens plenty of new clinically interesting opportunities. Therefore, organ-level segmentation of PET images has important applications, yet it lacks sufficient research. In this proof of concept study, we evaluate if the previously used segmentation approaches are suitable for segmenting dynamic human total-body PET images in organ level. Our focus is on general-purpose unsupervised methods that a

SUBMITTER: Jaakkola MK 

PROVIDER: S-EPMC10715853 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature