LIMANI

LIMANI quantifies leaf vascular networks from dark-field images to enable quantitative analysis of venation patterns and comparison across transgenic Arabidopsis lines.

Key Features:

• Automated Image Segmentation: Applies automated segmentation algorithms to extract venation patterns from dark-field leaf images.
• Quantitative Network Metrics: Computes vascular network metrics including numbers of end-points and branching points and network topology measurements.
• Sensitivity to Subtle Perturbations: Detects subtle alterations in vascular networks across genotypes and leaf traits.
• Support for Dynamic Modeling: Produces quantitative data suitable for testing and correlating with theoretical or dynamic models of venation patterning.
• Application to Arabidopsis: Enables quantitative comparison of venation in transgenic Arabidopsis lines with altered cellular and leaf size traits.

Scientific Applications:

• Vascular differentiation analysis: Quantitatively characterizes vascular differentiation during leaf development.
• Genotype-phenotype comparison: Compares venation patterns across transgenic Arabidopsis lines to link genetic perturbations with vascular outcomes.
• Pattern formation studies: Provides empirical data supporting investigation of self-organizing mechanisms underlying leaf venation.
• Spatial trait assessment: Assesses how vascular network balance between end-points and branching points correlates with leaf position on the rosette.

Methodology:

Captures dark-field images of leaves and processes them with automated segmentation algorithms to extract vascular network structures and quantify end-points, branching points, and related network metrics for correlation with theoretical models.