aceBrain fluorodeoxyglucose PET anatomical segmentation via AI: extensive validation in the neurodegenerative spectrum
The semi-quantitative assessment of brain [18F]FDG PET provides a more objective interpretation and improved accuracy in differentiating across neurodegenerative diseases. However, the correct identification of anatomical regions of interest without a structural MRI is challenging. Thus, this study aims to develop a deep-learning-based (DL-based) model for the automatic segmentation of 52 anatomical regions in brain [18F]FDG PET images and validate it across different metabolic profiles. 1628 brain [18F]FDG PET…
Deep-learning model automatically segmented 52 anatomical regions on brain FDG PET alone and achieved MRI-comparable pons-based SUVR quantification across cognitively normal and neurodegenerative patients.
Evidence
- Peer-reviewedBrain2026-09-15
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Truvace Impact Record TRV-2026-1110, v1: “Brain fluorodeoxyglucose PET anatomical segmentation via AI: extensive validation in the neurodegenerative spectrum.” Truvace, 2026-09-16. /record/TRV-2026-1110 (accessed at citation time). sha256 63df40f86dfdd887…
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