mouselungseg 0.0.3

3D U-Net model for the segmentation of the lungs in mice CT scans.

🫁 Lungs segmentation in mice CT scans

We provide a 3D U-Net model for the segmentation of the lungs region in mice CT scans.

[Installation] [Model weights] [Usage]

This project is part of a collaboration between the EPFL Center for Imaging and the De Palma Lab.

The implementation, training, and validation of the model were done by Quentin Chappuis during the course of his Bachelor Project in the Fall semester of 2023 under the supervision of Mallory Wittwer. The corresponding project that includes training notebooks is available in this repository.

Related projects:

• Mouse Tumor Net

Installation

We recommend performing the installation in a clean Python environment.

The code requires python>=3.9, as well as pytorch>=2.0.

Install our package from the repository:

pip install git+https://gitlab.com/center-for-imaging/lungs-segmentation.git

or clone the repository and install with:

git clone git+https://gitlab.com/center-for-imaging/lungs-segmentation.git
cd mouselungseg
pip install -e .

Model

Model weights The model weights (~1.1 GB) are automatically downloaded from this repository on Zenodo the first time you run inference. The model files are saved in the user home folder in the .lungsunet directory.

Training The model was trained using a dataset of 355 images coming from 17 different experiments, 2 different scanners and was validated on 62 images.

Usage

In Napari

To use our model in Napari, start the viewer with

napari -w mouselungseg

Open an image using File > Open files or drag-and-drop an image into the viewer window. If you want to open medical image formats such as NIFTI directly, consider installing the napari-medical-image-formats plugin.

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).

from mouselungseg import LungsPredict

lungs_predict = LungsPredict()
segmentation = lungs_predict.predict(your_image)
mask = lungs_predict.postprocess(segmentation)

As a CLI

Run inference on an image from the command-line. For example:

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:

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 model is licensed under the BSD-3 license.

Carbon footprint of this project

As per the online tool Green algorithms, the footprint of training this model was estimated to be around 584 g CO2e.