https://github.com/masilab/deep_fixel
DeepFixel: Deep learning-based identification of crossing fiber bundle elements
https://github.com/masilab/deep_fixel
Last synced: 11 days ago
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DeepFixel: Deep learning-based identification of crossing fiber bundle elements
- Host: GitHub
- URL: https://github.com/masilab/deep_fixel
- Owner: MASILab
- License: mit
- Created: 2025-03-03T17:42:24.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-04-24T18:40:43.000Z (9 months ago)
- Last Synced: 2025-04-24T19:38:59.456Z (9 months ago)
- Language: Python
- Size: 85.9 KB
- Stars: 0
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DeepFixel
Deep learning-based identification of crossing fiber bundle elements
## Training and testing the model
You can set up an environment using [`uv`](https://github.com/astral-sh/uv) by running the following command:
```bash
uv sync
```
To run the model, download the weights and testing dataset from the following link: [https://zenodo.org/records/16587458](https://zenodo.org/records/16587458).
- Unzip and copy the testing data to `./test_data`
- Put the weights in `./models/pretrained`
See `run_pretrained_deep_fixel.py` to test the pretrained model and `run_deep_fixel.py` to train and test a new model.
## Using the model
If you wish to apply the model to your own dataset, you can use `fissile.test_mesh_model()` as a basis for your code. You can also use `fissile.dataset.GeneratedMeshNIFTIDataset()` if your data is stored as spherical harmonic coefficients in a NIFTI file.