AI Article Synopsis

  • MOOSE software is designed for subject-specific, multiorgan segmentation using AI to enhance whole-body PET imaging research.
  • It was trained on data from 2 PET/CT systems, employing 50 whole-body CT images, including healthy and oncology patients, and 34 F-FDG PET/MRI brain image volumes for cerebral structures.
  • The segmentation performance was evaluated using the Dice score coefficient, with noncerebral tissues achieving an average Dice score over 0.90 in most cases, while cerebral segmentations showed lower scores, indicating variability in accuracy among different organs.

Article 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. 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 F-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 F-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. 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). The proposed segmentation pipeline allows automatic segmentation of 120 unique tissues from whole-body F-FDG PET/CT images with high accuracy.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9730926PMC
http://dx.doi.org/10.2967/jnumed.122.264063DOI Listing

Publication Analysis

Top Keywords

men women
16
segmentation
8
whole-body f-fdg
8
f-fdg pet/ct
8
pet/ct images
8
data-centric artificial
8
artificial intelligence
8
noncerebral structures
8
healthy controls
8
controls men
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!