Metadata-Version: 2.4
Name: gliographseg
Version: 0.1.2
Summary: Graph-based brain tumor segmentation using superpixels and GNNs
Author: Salvatore Calderaro
License-Expression: MIT
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch
Requires-Dist: torch-geometric
Requires-Dist: numpy
Requires-Dist: networkx
Requires-Dist: scikit-image
Requires-Dist: Pillow
Dynamic: license-file

![GlioGraphSeg Logo](GlioGraphSeg_logo.png)

# GlioGraphSeg

GlioGraphSeg is a deep learning-based tool for brain tumor segmentation from MRI scans.
It uses Graph Neural Networks to model spatial relationships between regions.
The system provides accurate glioma detection and segmentation.

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## Try the Streamlit App

👉 [Access GlioGraphSeg here](https://gliographseg.streamlit.app)

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## Description

GlioGraphSeg combines deep learning and graph-based modeling to improve the segmentation of gliomas in brain MRI scans.  
The tool is designed to support medical professionals by providing accurate, automated tumor delineation using GNNs.

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## Citation

If you use GlioGraphSeg in your research, please cite the following paper:

> Amato, D., Calderaro, S., Bosco, G. L., Rizzo, R., & Vella, F. (2024, December). Semantic Segmentation of Gliomas on Brain MRIs by Graph Convolutional Neural Networks. In 2024 International Conference on AI x Data and Knowledge Engineering (AIxDKE) (pp. 143-149). IEEE.
> [DOI link](https://ieeexplore.ieee.org/abstract/document/10990089)

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## Contact

For questions or collaborations, contact: [salvatore.calderaro01@unipa.it](mailto:salvatore.calderaro01@unipa.it)
