🐭 Lungs segmentation in mice CT scans

We provide a neural network model for segmenting the lungs of the mice. The model is based on the U-Net architecture.

The goal of our tool is to provid a reliable way to segment the lungs in mouse CT scans. The U-net model produces a binary mask representing the segmentation of the lungs.

Try the model

• Install the package
• Follow the usage instructions

Installation

We recommend performing the installation in a clean Python environment.

The code requires python>=3.9, as well as pytorch>=2.0. Please install Pytorch first and separately following the instructions for your platform on pytorch.org.

Install unet_lungs_segmentation using pip after you've installed Pytorch:

pip install unet_lungs_segmentation

or clone the repository and install with:

git clone https://github.com/qchapp/lungs-segmentation.git
pip install -e .

Models

The model weights (~1 GB) will be automatically downloaded from Hugging Face.

Usage in Napari

Napari is a multi-dimensional image viewer for python. To use our model in Napari, start the viewer with

napari

To open an image, use 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 (unet_lungs_segmentation). Select an image and run it by pressing the "Segment lungs" button.

Usage 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 unet_lungs_segmentation import LungsPredict

lungs_predict = LungsPredict()
mask = lungs_predict.segment_lungs(your_image)

or if you want to apply a specific threshold (float between 0 and 1):

mask = lungs_predict.segment_lungs(your_image, threshold)

Usage as a CLI

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

uls_predict_image -i /path/to/folder/image_001.tif [-t <threshold>]

The <threshold> will be applied to the predicted image in order to have a binary mask. A default threshold of 0.5 will be applied if none is given. Should be a float between 0 and 1.

The command will save the segmentation next to the image:

folder/
    ├── image_001.tif
    ├── image_001_mask.tif

Run inference in batch on all images in a folder:

uls_predict_folder -i /path/to/folder/ [-t <threshold>]

Will produce:

folder/
    ├── image_001.tif
    ├── image_001_mask.tif
    ├── image_002.tif
    ├── image_002_mask.tif

Dataset

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

Issues

If you encounter any problems, please fill 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.