---
license: mit
tags:

- medical

- neuroimaging

- fdg-pet

- brain-segmentation

- deep-learning

- 3d-segmentation
datasets:

- adni

- nacc

- oasis3

- 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.

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).

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)