AI-based quantification of whole-body tumour burden on somatostatin receptor PET/CT.

Anni GålneOlof EnqvistAnna SundlövKristian ValindDavid MinarikElin Trägårdh

Published in: European journal of hybrid imaging (2023)

It was possible to develop an AI model to segment SRETVwb and TLSREwb with high performance. A fully automated method makes quantification of tumour burden achievable and has the potential to be more widely used when assessing PET/CT images.

Keyphrases

pet ct
artificial intelligence
deep learning
machine learning
positron emission tomography
risk factors
high throughput
optical coherence tomography
computed tomography
human health
risk assessment
climate change