TY - JOUR T1 - Fully Automated, Semantic Segmentation of Whole-Body <sup>18</sup>F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence JF - Journal of Nuclear Medicine JO - J Nucl Med SP - 1941 LP - 1948 DO - 10.2967/jnumed.122.264063 VL - 63 IS - 12 AU - Lalith Kumar Shiyam Sundar AU - Josef Yu AU - Otto Muzik AU - Oana C. Kulterer AU - Barbara Fueger AU - Daria Kifjak AU - Thomas Nakuz AU - Hyung Min Shin AU - Annika Katharina Sima AU - Daniela Kitzmantl AU - Ramsey D. Badawi AU - Lorenzo Nardo AU - Simon R. Cherry AU - Benjamin A. Spencer AU - Marcus Hacker AU - Thomas Beyer Y1 - 2022/12/01 UR - http://jnm.snmjournals.org/content/63/12/1941.abstract N2 - 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, visceral fat, and skeletal muscle, which were semiautomatically segmented using thresholding. A majority-voting algorithm was used to generate a reference-standard segmentation. From the 50 CT datasets, 40 were used for training and 10 for testing. For cerebral structures, 34 18F-FDG PET/MRI brain image volumes were used from 10 healthy controls (5 men and 5 women imaged twice) and 14 nonlesional epilepsy patients (7 men and 7 women). Only 18F-FDG PET images were considered for training: 24 and 10 of 34 volumes were used for training and testing, respectively. The Dice score coefficient (DSC) was used as the primary metric, and the average symmetric surface distance as a secondary metric, to evaluate the automated segmentation performance. Results: An excellent overlap between the reference labels and MOOSE-derived organ segmentations was observed: 92% of noncerebral tissues showed DSCs of more than 0.90, whereas a few organs exhibited lower DSCs (e.g., adrenal glands [0.72], pancreas [0.85], and bladder [0.86]). The median DSCs of brain subregions derived from PET images were lower. Only 29% of the brain segments had a median DSC of more than 0.90, whereas segmentation of 60% of regions yielded a median DSC of 0.80–0.89. The results of the average symmetric surface distance analysis demonstrated that the average distance between the reference standard and the automatically segmented tissue surfaces (organs, bones, and brain regions) lies within the size of image voxels (2 mm). Conclusion: The proposed segmentation pipeline allows automatic segmentation of 120 unique tissues from whole-body 18F-FDG PET/CT images with high accuracy. ER -