Open-source scientific software
AxoDen
A tool for quantifying labelled axonal density in microscopy images, built to make a research workflow accessible and reproducible.
The problem
Quantifying labelled projections means making choices about signal, background and regions of interest. Those choices need to be consistent enough to support comparisons and visible enough for other researchers to inspect.
The tool
AxoDen uses Otsu thresholding to separate image signal from background and quantify labelled axonal density within defined regions. Its interfaces let researchers use the method through a browser, a desktop application or Python.
My role
I designed the research, developed the method and contributed to experiments, analysis and writing as first author. The project was a collaboration with Emmy Li, Oliver Joseph, Pascal A. Dufour and Gregory Corder; software and research contributions were shared across the team.
Evidence and boundaries
The peer-reviewed paper describes and evaluates the method. The repository provides source code and example images. These make it possible to inspect the approach and try it on representative data before using it in a new study.
A threshold-based measurement still depends on image quality, region selection and acquisition conditions. A density estimate alone does not establish axon counts or functional connectivity. Comparisons need an acquisition and analysis plan that fits the biological question.
Start with the repository’s test_images folder and follow the current documentation. Record the version, image preparation and region choices so the analysis can be repeated.
Publication
Sandoval Ortega RA, Li E, Joseph O, Dufour PA, Corder G. AxoDen: An Algorithm for the Automated Quantification of Axonal Density in Defined Brain Regions. eNeuro, 2025.
Read the paper ↗ · Open-access full text ↗
What this project brings together
Scientific problem definition, quantitative image analysis, collaborative software development and communication for both experimental and computational users.
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