Toward High-Throughput Artificial Intelligence-Based Segmentation in Oncological PET Imaging.

Artificial intelligence (AI) techniques for image-based segmentation have garnered much attention in recent years. Convolutional neural networks have shown impressive results and potential toward 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 bimodality scans. This work reviews existing AI techniques for segmentation tasks and the evaluation criteria for translational AI-based segmentation efforts toward routine adoption in clinical workflows.

Informations de copyright

Copyright © 2021 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Disclosure The authors do not have anything to disclose regarding conflict of interest with respect to this article.