🧬 Tau Fibrils Yolo - Object detection in EM images

We provide a YoloV8 model for the detection of oriented bounding boxes (OBBs) of Tau fibrils in EM images. The model is integrated as a Napari plugin.

[Installation] [Model] [Usage] [Training]

This project is part of a collaboration between the EPFL Center for Imaging and the Laboratory of Biological Electron Microscopy.

Installation

We recommend performing the installation in a clean Python environment. Install the package from PyPi:

pip install tau-fibrils-yolo

or from the repository:

pip install git+https://gitlab.com/center-for-imaging/tau-fibrils-object-detection.git

or clone the repository and install with:

git clone https://github.com/EPFL-Center-for-Imaging/tau-fibrils-yolo.git
cd tau-fibrils-yolo
pip install -e .

Usage

In Napari

To use the model in Napari, start the viewer with

napari -w tau-fibrils-yolo

or open the plugin from Plugins > Tau fibrils detection.

From the command-line

Run inference on an image from the command-line:

tau_fibrils_predict_image -i /path/to/folder/image_001.tif

This command will run the YOLO model and save a CSV file containing measurements next to the image:

folder/
    ├── image_001.tif
    ├── image_001_results.csv

Training

The instructions for training the model can be found here.

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.

This project depends on the ultralytics package which is licensed under AGPL-3.

Acknowledgements

We would particularly like to thank Valentin Vuillon for annotating the images on which this model was trained, and for developing the preliminary code that laid the foundation for this image analysis project. The repository containing his original version of the project can be found here.