| --- |
| license: mit |
| tags: |
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| - medical |
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| - neuroimaging |
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| - fdg-pet |
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| - brain-segmentation |
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| - deep-learning |
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| - 3d-segmentation |
| datasets: |
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| - adni |
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| - nacc |
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| - oasis3 |
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| - nifd |
| pipeline_tag: image-segmentation |
| library_name: pytorch |
| --- |
| # FDG-NeuroSegmenter |
| <b>FDG-NeuroSegmenter</b> is a deep-learning-based model developed to perform the automatic segmentation of 52 |
| anatomical regions in brain [<sup>18</sup>F]FDG PET images. |
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| Here you can find the [nnU-Net](https://github.com/MIC-DKFZ/nnUNet) models! For more information and other resources |
| check out the [repository on GitHub](https://github.com/NM-Radiopharmacology/FDG-NeuroSegmenter.git). |
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| If you use the FDG-NeuroSegmenter models in your research, please cite our paper: |
| > **Brain Fluorodeoxyglucose PET Anatomical Segmentation via AI: Extensive Validation in the Neurodegenerative Spectrum** |
| > Luísa C. Silva, Francisco P. M. Oliveira and Durval C. Costa for the Alzheimer's Disease Neuroimaging Initiative and for the Frontotemporal Lobar Degeneration Neuroimaging Initiative |
| > *Brain* (2026) |
| > DOI:[10.1093/brain/awag314](https://doi.org/10.1093/brain/awag314) |
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