https://github.com/epfl-center-for-imaging/mouselungseg
Lungs segmentation in mice CT scans
https://github.com/epfl-center-for-imaging/mouselungseg
cancer-imaging-research image-segmentation napari-plugin ultralytics yolo
Last synced: 11 months ago
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Lungs segmentation in mice CT scans
- Host: GitHub
- URL: https://github.com/epfl-center-for-imaging/mouselungseg
- Owner: EPFL-Center-for-Imaging
- License: agpl-3.0
- Created: 2024-01-18T19:07:13.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-23T13:55:12.000Z (almost 2 years ago)
- Last Synced: 2025-07-23T18:34:45.193Z (about 1 year ago)
- Topics: cancer-imaging-research, image-segmentation, napari-plugin, ultralytics, yolo
- Language: Python
- Homepage:
- Size: 806 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🫁 Lungs segmentation in mice CT scans
We provide a [YoloV8](https://docs.ultralytics.com/) model for the segmentation of the lungs region in mice CT scans. The model was trained on 2D slices and can be applied slice by slice to produce 3D segmentations.
[[`Installation`](#installation)] [[`Model weights`](#model)] [[`Usage`](#usage)]
This project is part of a collaboration between the [EPFL Center for Imaging](https://imaging.epfl.ch/) and the [De Palma Lab](https://www.epfl.ch/labs/depalma-lab/).
## Installation
We recommend performing the installation in a clean Python environment. Install our package from PyPi:
```sh
pip install mouselungseg
```
or from the repository:
```sh
pip install git+https://github.com/EPFL-Center-for-Imaging/mouselungseg.git
```
or clone the repository and install with:
```sh
git clone git+https://github.com/EPFL-Center-for-Imaging/mouselungseg.git
cd mouselungseg
pip install -e .
```
## Model weights
The model weights (~6 Mb) are automatically downloaded from [this repository on Zenodo](https://zenodo.org/records/13268683) the first time you run inference. The model files are saved in the user home folder in the `.mousetumornet` directory.
## Usage
**In Napari**
To use our model in Napari, start the viewer with
```sh
napari -w mouselungseg
```
Open an image using `File > Open files` or drag-and-drop an image into the viewer window.
**Sample data**: To test the model, you can run it on our provided sample image. In Napari, open the image from `File > Open Sample > Mouse lung CT scan`.
Next, in the menu bar select `Plugins > Lungs segmentation (mouselungseg)` to start our plugin.
**As a library**
You can run a model in just a few lines of code to produce a segmentation mask from an image (represented as a numpy array).
```py
from mouselungseg import LungsPredictor
lungs_predict = LungsPredictor()
segmentation = lungs_predict.predict(your_image)
```
**As a CLI**
Run inference on an image from the command-line. For example:
```sh
uls_predict_image -i /path/to/folder/image_001.tif
```
The command will save the segmentation next to the image:
```
folder/
├── image_001.tif
├── image_001_mask.tif
```
To run inference in batch on all images in a folder, use:
```sh
uls_predict_folder -i /path/to/folder/
```
This will produce:
```
folder/
├── image_001.tif
├── image_001_mask.tif
├── image_002.tif
├── image_002_mask.tif
```
## Issues
If you encounter any problems, please file an issue along with a detailed description.
## License
This project is licensed under the [AGPL-3](LICENSE) license.
This project depends on the [ultralytics](https://github.com/ultralytics/ultralytics) package which is licensed under AGPL-3.
## Related projects
- [Mouse Tumor Net](https://github.com/EPFL-Center-for-Imaging/mousetumornet) | Detect tumor nodules in mice CT scans.
- [Mouse Tumor Track](https://github.com/EPFL-Center-for-Imaging/mousetumortrack) | Track tumor nodules in mice CT scans.
## Acknowledgements
Special thanks go to **Quentin Chappuis** for his contribution to the ideation and exploration of the data and for developing the preliminary code that laid the foundations for this project during the course of his Bachelor project in Fall 2023.