Graph-based brain tumor segmentation using superpixels and GNNs
Project description
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.
Try the Streamlit App
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.
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
Contact
For questions or collaborations, contact: salvatore.calderaro01@unipa.it
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file gliographseg-0.1.2.tar.gz.
File metadata
- Download URL: gliographseg-0.1.2.tar.gz
- Upload date:
- Size: 25.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
3d271be47ab289b5fcba7a34ab91fc0be837664bed576dfdefca5bd9024e287c
|
|
| MD5 |
09eb4f5c7de9e4ff741668e8618e56a9
|
|
| BLAKE2b-256 |
61d84579e1ba863236d629866bb11c6ec35196fe1ee6457d7401e6db069f4bb2
|
File details
Details for the file gliographseg-0.1.2-py3-none-any.whl.
File metadata
- Download URL: gliographseg-0.1.2-py3-none-any.whl
- Upload date:
- Size: 24.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
4c1e0168481011f10f68748c1eca7e42a7e288c7bef5ae05e7131b5b4882b687
|
|
| MD5 |
487b4ee607208783bdaf792aca92e461
|
|
| BLAKE2b-256 |
23076bce18374ce14f4ee74717a8293eb0906258c47e4b3b5f90a0aa478766ad
|
