On the value of graph-based segmentation for the analysis of structural networks in life sciences

We propose, under the form of a short overview, to stress the interest of graph to encode the "topological" structure of networks hidden in images especially when applied in life sciences. We point toward existing computer science tools to extract such structural graph from images. We then illustrate different applications, such as segmentation, denoising, and simulation on practical examples of various bioimaging domains including vascular networks observed with fluorescent microscopy in 2D imaging, macroscopic root systems observed in 2D optical intensity imaging, and 3D porosity networks of seed observed in absorption X-ray microtomography