img2net: Automated network-based analysis of imaged phenotypes

TL;DR

img2net is an open-source tool that automates the analysis of complex network-like structures in images, enabling reconstruction, property computation, and comparison across different conditions.

Contribution

The paper introduces img2net, a versatile software for automated analysis of complex 2D and 3D network structures in biological images, addressing a gap in current methods.

Findings

Successfully reconstructs underlying networks from images.

Computes relevant network properties for biological structures.

Allows statistical comparison of networks under different conditions.

Abstract

Automated analysis of imaged phenotypes enables fast and reproducible quantification of biologically relevant features. Despite recent developments, recordings of complex, networked structures, such as: leaf venation patterns, cytoskeletal structures, or traffic networks, remain challenging to analyze. Here we illustrate the applicability of img2net to automatedly analyze such structures by reconstructing the underlying network, computing relevant network properties, and statistically comparing networks of different types or under different conditions. The software can be readily used for analyzing image data of arbitrary 2D and 3D network-like structures. img2net is open-source software under the GPL and can be downloaded from http://mathbiol.mpimp-golm.mpg.de/img2net/, where supplementary information and data sets for testing are provided.