ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2022
Aortic wall segmentation in
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Review
- NaF-PET/CT imaging of atherosclerosis in type 2 diabetes: Associations with sex and history of cardiovascular events in a 2-year follow-up study.European journal of nuclear medicine and molecular imaging · 2025Article
- Auto-segmentation, radiomic reproducibility, and comparison of radiomics between manual and AI-derived segmentations for coronary arteries in cardiac [EJNMMI physics · 2025Article
- Common carotid segmentation inClinical physiology and functional imaging · 2023Article
- An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images.Biomolecules · 2023Article
- NaF-PET Imaging of Atherosclerosis Burden.Journal of imaging · 2023Review
- PET-Based Imaging withDiagnostics (Basel, Switzerland) · 2021Review
- Two-year change in 18F-sodium fluoride uptake in major arteries of healthy subjects and angina pectoris patients.The international journal of cardiovascular imaging · 2021Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe aimed to establish and test an automated AI-based method for rapid segmentation of the aortic wall in positron emission tomography/computed tomography (PET/CT) scans.
methodsFor segmentation of the wall in three sections: the arch, thoracic, and abdominal aorta, we developed a tool based on a convolutional neural network (CNN), available on the Research Consortium for Medical Image Analysis (RECOMIA) platform, capable of segmenting 100 different labels in CT images. It was tested on
resultsCNN-derived values for volume, SUVmax, and SUVtotal were all slightly, i.e., 13-17%, lower than the corresponding manually obtained ones, whereas SUVmean values for the three aortic sections were virtually identical for the two methods. Manual segmentation lasted typically 1-2 hours per scan compared to about one minute with the CNN-based approach. The maximal deviation at repeat manual segmentation was 6%.
conclusionsThe automated CNN-based approach was much faster and provided parameters that were about 15% lower than the manually obtained values, except for SUVmean values, which were comparable. AI-based segmentation of the aorta already now appears as a trustworthy and fast alternative to slow and cumbersome manual segmentation.
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