Brain fluorodeoxyglucose PET anatomical segmentation via AI: extensive validation in the neurodegenerative spectrum

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ID: 328981
2026
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Abstract
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 images of 1099 subjects were included in the internal dataset, comprising cognitively normal subjects (n=537), and patients with mild cognitive impairment (n=538), subjective memory concerns (n=70), Alzheimer's disease (n=330), frontotemporal lobar degeneration (n=95) and Lewy body dementia (n=58). Train-test split yielded 1109/519 images (631/468 subjects) for training and internal testing of a DL-based model, respectively. An additional dataset of 108 [18F]FDG PET images was included for external validation. Ground-truth segmentation was performed on the paired T1-weighted MRI image for each [18F]FDG PET image, for a total of 52 anatomical regions of interest. Atlas-based segmentation was used as a benchmark. The Dice similarity coefficient (DSC) was used to assess segmentation performance. Agreement in mean pons-based standardised uptake value ratio (SUVRmean) quantification was assessed through the intraclass correlation coefficient (ICC) and relative deviation in absolute value. Per-region mean DSC for the DL-based segmentations ranged from 0.729 to 0.923 in the internal test set. Global mean DSC was 0.84±0.06. SUVRmean quantification of the different regions using the DL-based segmentation masks showed strong agreement with that obtained using the ground-truth segmentation masks, with an average ICC of 0.96±0.02. The per-region average of relative SUVRmean deviation did not exceed 5%. DL-based segmentation significantly outperformed atlas-based segmentation (p<0.05). Similar segmentation performance was obtained in the external validation dataset. DL-based anatomical segmentation of brain [18F]FDG PET proved to be robust across a wide spectrum of neurodegenerative diseases. Semi-quantitative assessment was comparable with that obtained with MRI-based segmentation. DL-based segmentation, therefore, proved to be a reliable alternative when MRI isn't available, and is a better option than the commonly used atlas-based approach.
Reference Key
openalex_W7213337003 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Luísa Carvalho Silva, Francisco P. M. Oliveira, Durval C. Costa
Journal Brain research
Year 2026
DOI
10.1093/brain/awag314
URL
https://doi.org/10.1093/brain/awag314
Keywords Keywords not found

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