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Welcome to an automated Segmentation-Tool for multi modal Magnetic Resonance Images. The goal is to generate multiclass segmentations of T1-weighted and T2-weighted scans. The SPIDER Dataset is used for training the models.
├── LICENSE
├── Makefile <- Makefile with commands like `make dirs` or `make clean`
├── README.md <- The top-level README for developers using this project.
├── data.dvc <- Keeps the raw data versioned.
├── data
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── dvc.lock
├── dvc.yaml
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── docs <- Data dictionaries, manuals, and all other explanatory materials.
│
├── results <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│ └── metrics.json <- Relevant metrics after evaluating the model.
│ └── training_metrics.json <- Relevant metrics from training the model.
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ ├── __init__.py
│ │ └── make_dataset.py
│ │
│ ├── models <- Scripts to train models and architectures
│ │ │ ├── metrics
│ │ │ ├── modules
│ │ │ └── networks
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ ├── __init__.py
│ └── utils.py
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
Welcome to an automated Segmentation-Tool for multi modal Magnetic Resonance Images. The goal is to generate multiclass segmentations of T1-weighted and T2-weighted scans. The SPIDER Dataset is used for training the models.
This is an example of how to list things you need to use the software and how to install them.
- python >=3.8
Use the package manager pip to install foobar.
pip install foobarimport spinesegdiffHow to train the model: To train the model, please run the following command, you can change the parameters within the train.py file.
python -u src\trainer.py -e 150
*** Default training parameteres ***
parser.add_argument("-lr", help="set the learning rate for the unet", type=float, default=0.0001)
parser.add_argument("-e", "--epochs", help="the number of epochs to train", type=int, default=300)
parser.add_argument("-bs", "--batch_size", help="batch size of training", type=int, default=4)
parser.add_argument("-pt", "--pretrained", help="whether to train from scratch or resume", action="store_true",
default=False)
- Feature 1
- Feature 2
- Feature 3
- Nested Feature 3.1
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the Apache License Version 2.0, License. See LICENSE for more information.
Maria Monzon - Emai Me