Introduction
The MyoFInDer Software (The Myoblast Fusion Index Determination Software) is a Python module for the automatic calculation of the fusion index on fluorescence-stained microscopy images of muscle cell cultures. It provides a simple and user-friendly interface for managing multiple images as a project. In the interface, users can open and save projects, tune settings, start computations, and manually correct the output.
On this documentation website, you can find information about the :
[!IMPORTANT] MyoFInDer is currently in maintenance-only development phase. This means that reported bugs will be fixed, minor changes will be brought to support new Python versions if possible, but no major improvements or updates should be expected. User requests for new features could still be addressed, depending on how large they are.
[!WARNING] MyoFInDer version 1.1.0 now uses CellPose for nuclei segmentation instead of DeepCell. This is a major breaking change. Differences are to be expected in the results obtained with version 1.1.0 and earlier ones, even with similar processing parameters.
About
This module is free and open-source, hosted and distributed on GitHub under the GNU GPL-3.0 license. It was developed at the Tissue Engineering Lab, part of the KU Leuven KULAK university. Contributions, bug reports or simple questions are welcome in the dedicated sections of the GitHub page.
Citing
If MyoFInDer has been of help in your research, please reference it in your academic publications by citing the following article:
- Weisrock A., Wüst R., Olenic M. et al., MyoFInDer: An AI-Based Tool for Myotube Fusion Index Determination, Tissue Eng. Part A (30), 19-20, 2024, DOI: 10.1089/ten.TEA.2024.0049. (link to Weisrock et al.)
Here is the BibTex code for referencing the article:
@article{Weisrock2024,
author = {Weisrock, Antoine and W\"{u}st, Rebecca and Olenic, Maria and Lecomte-Grosbras, Pauline and Thorrez, Lieven},
title = {MyoFInDer: An AI-Based Tool for Myotube Fusion Index Determination},
journal = {Tissue Engineering Part A},
volume = {30},
number = {19-20},
pages = {652-661},
year = {2024},
doi = {10.1089/ten.tea.2024.0049},
}