File size: 1,118 Bytes
7f03f91 237be1b 2209ddb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ---
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)
|