Fully Automated, Semantic Segmentation of Whole-Body ¹⁸F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence.

ABSTRACT: We introduce multiple-organ objective segmentation (MOOSE) software that generates subject-specific, multiorgan segmentation using data-centric artificial intelligence principles to facilitate high-throughput systemic investigations of the human body via whole-body PET imaging. Methods: Image data from 2 PET/CT systems were used in training MOOSE. For noncerebral structures, 50 whole-body CT images were used, 30 of which were acquired from healthy controls (14 men and 16 women), and 20 datasets were acquired from oncology patients (14 men and 6 women). Noncerebral tissues consisted of 13 abdominal organs, 20 bone segments, subcutaneous fat, visceral fat, psoas muscle, and skeletal muscle. An expert panel manually segmented all noncerebral structures except for subcutaneous fat, visc

SUBMITTER: Shiyam Sundar LK 

PROVIDER: S-EPMC9730926 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature