Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET.

ABSTRACT:

Background

Positron emission tomography (PET) is routinely used for cancer staging and treatment follow-up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV-including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge.

Methods

In this study, we compare two semi-automatic artificial intelligence (AI)-based segmentation methods with conventional semi-automatic segmentation approaches in terms of repeatability. One s

SUBMITTER: Pfaehler E 

PROVIDER: S-EPMC7788118 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature