# Prostate Segmentation MoE
A simple Python module for easy segmentation of the prostate in T2-weighted MRI images in NIfTI format. This module utilizes a mixture of U-Net architectures for segmentation tasks and aims to provide a straightforward solution for users working with prostate MRI data.
## Features
- Segmentation of prostate regions in T2-weighted MRI images.
- Uses a combination of trained U-Net models for accurate and efficient segmentation.
- Supports input in the NIfTI format, a common format for medical imaging.
- Returns 3D masks aswell as the volume estimation.
## Recommended before installation (Windows)
### Install virtualenv if not already
```pip install virtualenv```
### Create a virtual environment
```python -m venv venv```
### Activate it
```venv\Scripts\activate```
- You may need to run ```Set-ExecutionPolicy Unrestricted -Scope Process``` before activation
- If creating your own repository don't forget to ignore the `venv` folder in the `.gitignore` file
## Installation
Install the library using pip:
```pip install psmoe```
## Usage
### Example
Check for `example.py`
### Scripted Download
You can also download a T2-weighted MRI sequence using the provided function in the example.
## License
This project is licensed under the MIT License - see the LICENSE file for details.
For more details, visit the [GitHub repository](https://github.com/mpierangeli/prostate_segmentation_moe).
Raw data
{
"_id": null,
"home_page": "https://github.com/mpierangeli/prostate_segmentation_moe",
"name": "psmoe",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "UNet,ML,Prostate,Segmentations",
"author": "Martin Pierangeli",
"author_email": "marespierangeli@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/1a/a1/ee8315c7c3296b1693182278ea9247456e37fd7f982061e6fe0467e78d24/psmoe-0.1.4.5.tar.gz",
"platform": null,
"description": "# Prostate Segmentation MoE\r\n\r\nA simple Python module for easy segmentation of the prostate in T2-weighted MRI images in NIfTI format. This module utilizes a mixture of U-Net architectures for segmentation tasks and aims to provide a straightforward solution for users working with prostate MRI data.\r\n\r\n## Features\r\n\r\n- Segmentation of prostate regions in T2-weighted MRI images.\r\n- Uses a combination of trained U-Net models for accurate and efficient segmentation.\r\n- Supports input in the NIfTI format, a common format for medical imaging.\r\n- Returns 3D masks aswell as the volume estimation.\r\n\r\n## Recommended before installation (Windows)\r\n\r\n### Install virtualenv if not already\r\n```pip install virtualenv```\r\n### Create a virtual environment\r\n```python -m venv venv```\r\n### Activate it\r\n```venv\\Scripts\\activate```\r\n- You may need to run ```Set-ExecutionPolicy Unrestricted -Scope Process``` before activation\r\n- If creating your own repository don't forget to ignore the `venv` folder in the `.gitignore` file\r\n\r\n\r\n## Installation\r\n\r\nInstall the library using pip:\r\n\r\n```pip install psmoe```\r\n\r\n## Usage\r\n### Example\r\nCheck for `example.py` \r\n### Scripted Download\r\nYou can also download a T2-weighted MRI sequence using the provided function in the example.\r\n\r\n## License\r\nThis project is licensed under the MIT License - see the LICENSE file for details.\r\n\r\n\r\nFor more details, visit the [GitHub repository](https://github.com/mpierangeli/prostate_segmentation_moe).\r\n\r\n",
"bugtrack_url": null,
"license": "",
"summary": "A simple module for prostate segmentation of T2-W MRI sequences in Nifti format",
"version": "0.1.4.5",
"project_urls": {
"Homepage": "https://github.com/mpierangeli/prostate_segmentation_moe",
"Source": "https://github.com/mpierangeli/prostate_segmentation_moe"
},
"split_keywords": [
"unet",
"ml",
"prostate",
"segmentations"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "0c775722f863430ceadf7dd1453f2a562662389faab80724123bea506a6d3319",
"md5": "792741d76626863f6a8cc1e9e96e0f64",
"sha256": "7712951111a58a84b717089d14533d9f38ee988824e33f452bf14edeab496364"
},
"downloads": -1,
"filename": "psmoe-0.1.4.5-py3-none-any.whl",
"has_sig": false,
"md5_digest": "792741d76626863f6a8cc1e9e96e0f64",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 9518,
"upload_time": "2024-01-22T16:02:36",
"upload_time_iso_8601": "2024-01-22T16:02:36.109508Z",
"url": "https://files.pythonhosted.org/packages/0c/77/5722f863430ceadf7dd1453f2a562662389faab80724123bea506a6d3319/psmoe-0.1.4.5-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "1aa1ee8315c7c3296b1693182278ea9247456e37fd7f982061e6fe0467e78d24",
"md5": "3f468f6e60b76ae3fa5b3decd7295f26",
"sha256": "b1a894604ff309e21f287b0cdabb2008f8b49e06bc8b21bcb382b5f4c79848d7"
},
"downloads": -1,
"filename": "psmoe-0.1.4.5.tar.gz",
"has_sig": false,
"md5_digest": "3f468f6e60b76ae3fa5b3decd7295f26",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 9917,
"upload_time": "2024-01-22T16:02:37",
"upload_time_iso_8601": "2024-01-22T16:02:37.207462Z",
"url": "https://files.pythonhosted.org/packages/1a/a1/ee8315c7c3296b1693182278ea9247456e37fd7f982061e6fe0467e78d24/psmoe-0.1.4.5.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-01-22 16:02:37",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "mpierangeli",
"github_project": "prostate_segmentation_moe",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [],
"lcname": "psmoe"
}
JSON
