Supplementary Materials for "Multi-Temporal Frames Projection for Dynamic Processes Fusion in Fluorescence Microscopy"

Repository Overview This Zenodo repository contains the supplementary materials supporting the study: “Multi-Temporal Frames Projection for Dynamic Processes Fusion in Fluorescence Microscopy”. The files included are: framesFusion.py Source code implementing the proposed framework for multi-temporal frames fusion. A link to the live GitHub repository is also provided below for version tracking and updates. Example_HL-1_(fluorescence).aviSample fluorescence microscopy data used as input for the algorithm. This is one of the original fluorescence microscopy input files from the microscope. Example_HL-1_(fluorescence).mp4 This file is a compressed .mp4 version of the original .avi file, provided for easier preview and broader compatibility across modern devices and platforms. While more convenient for viewing, it retains less information than the .avi file, and we do not recommend using it for testing (in the context of our study), despite being an acceptable format for our framework. Example_HL-1_(bright-field).mp4Companion bright-field video provided for interpretative purposes only (to visualise cell morphology and topology). Note: This file is not an input for our algorithm. SP_vs_AP_t-test.pdfDetailed statistical comparison of Sum Projection (SP) versus Average Projection (AP) across all processing method combinations described in the study. How to Use Use the Python file (famesFusion.py) together with the fluorescence sample video (Example_HL-1_(fluorescence).avi) to reproduce the steps and results described in the article. Detailed "How To" instructions (for any OpenCV-compatible dataset) are available in the GitHub repository. If you use this code or any material in this repository, please cite the associated work (DOI: 10.48550/arXiv.2601.10392) alongside the repository in line with the CC BY 4.0 principles.