Comparison of organ volumes and standardized uptake values in [18F]FDG‐PET/CT images using MOOSE and TotalSegmentator to segment CT images
The two software tools produce similar results in volume estimates for most anatomical structures. SUVmean is less dependent on the segmentation algorithm than SUVmax and SUVpeak and shows excellent reproducibility for all anatomical structures studied except for the bladder, the lungs and the skull.
Authors
- Lalith Kumar Shiyam Sundar (ORCID: https://orcid.org/0000-0003-3004-7937)
- Fanny Orlhac (ORCID: https://orcid.org/0000-0002-5588-1867)
- Nina Jehanno (ORCID: https://orcid.org/0000-0002-4370-8223)
- Romain‐David Seban (ORCID: https://orcid.org/0000-0002-2584-5478)
- Christophe Nioche (ORCID: https://orcid.org/0000-0002-6247-2025)
- Thomas Beyer (ORCID: https://orcid.org/0000-0002-8308-3918)
- Irène Buvat (ORCID: https://orcid.org/0000-0002-7053-6471)
- Narinée Hovhannisyan‐Baghdasarian (ORCID: https://orcid.org/0000-0003-1573-0703)
- C. Loisel
- Julie Auriac (ORCID: https://orcid.org/0000-0002-9165-3001)
Institutions
- Inserm (FR)
- Université Paris Sciences et Lettres (FR)
- Medical University of Vienna (AT)
- Institut Curie (FR)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1002/mp.70025
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Agence Nationale de la Recherche