  Artificial intelligence (AI) techniques for image-based segmentation have
garnered much attention in recent years. Convolutional neural networks (CNNs)
have shown impressive results and potential towards fully automated
segmentation in medical imaging, and particularly PET imaging. To cope with the
limited access to annotated data needed in supervised AI methods, given tedious
and prone-to-error manual delineations, semi-supervised and unsupervised AI
techniques have also been explored for segmentation of tumors or normal organs
in single and bi-modality scans. This work provides a review of existing AI
techniques for segmentation tasks and the evaluation criteria for translational
AI-based segmentation efforts towards routine adoption in clinical workflows.
