Algorithm to analyze the morphology and topology of 2D and 3D images of microvascular networks (Politecnico di Milano)
Active contributors: Alberto Rota
Supervisor: Luca Possenti
Copyright: Alberto Rota, Luca Possenti
Mailto: alberto1.rota@polimi.it , luca.possenti@polimi.it
μVES is an algorithm that autonomously analyses 2D and 3D images of microvascular networks. It provides a complete morphological analysis with information on the vessel's length, radius, tortuosity and eccentricity, as well as on the network topology with connectivity, bifurcations and dead ends.
From the raw microscopy data, muVES outputs a the segmented network, its skeleton, it connectivity map and a table with all morphological and topological information regarding each vessel.
| 2D Projection | Segmentation | Skeletonization | Connectivity |
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| Length map | Tortuosity map | Eccentricity map | |
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μVES was designed to analyze 3D images of microvascular networks. However, its features have been adapted to work with 2D images as well.
The 2D version of this algorithm outputs the same information as the 3D version, with the exception of:
- Z coordinates are never present
- Vessel Eccentricities cannot be calculated
- The lateral area is only estimated, assuming perfectly circular cross sections
A detailed illustrated guide on how to use μVES, including software requirements and dependencies, is provided at this repo's wiki
If you use μVES in your research, please cite the following paper:
@article{rota2023muves,
author = {Rota, Alberto and Possenti, Luca and Offeddu, Giovanni S. and Senesi, Martina and Stucchi, Adelaide and Venturelli, Irene and Rancati, Tiziana and Zunino, Paolo and Kamm, Roger D. and Costantino, Maria Laura},
title = {A three-dimensional method for morphological analysis and flow velocity estimation in microvasculature on-a-chip},
journal = {Bioengineering \& Translational Medicine},
volume = {n/a},
number = {n/a},
pages = {e10557},
keywords = {3D computational analysis, deep learning, network morphology, segmentation, vasculature-on-a-chip},
doi = {https://doi.org/10.1002/btm2.10557},
url = {https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/btm2.10557},
}