img2net

img2net reconstructs networks from 2D and 3D image-based phenotypic data and quantifies network properties to enable statistical comparisons of biological networked structures such as leaf venation and cytoskeletal arrangements.

Key Features:

• Automated Network Reconstruction: Processes 2D and 3D image datasets to reconstruct underlying network structures.
• Quantification of Biological Features: Performs fast and reproducible quantification of biologically relevant network features.
• Computation of Network Properties: Computes a range of structural and network properties essential for understanding biological systems.
• Statistical Comparison Capabilities: Enables robust statistical comparisons between different network types or networks under varying conditions.

Scientific Applications:

• Phenotypic Analysis: Provides automated and reproducible quantification of networked structures for phenotypic studies.
• Biological Research: Applicable to studies in plant biology (leaf venation), cellular biology (cytoskeletal structures), and systems biology involving complex networks.

Methodology:

img2net employs advanced algorithms to reconstruct network models from image data and analyze their structural properties, supporting both 2D and 3D datasets.