ILEE

ILEE computes quantitative, biologically interpretable indices of cytoskeletal organization from 2D and 3D fluorescence images using an implicit Laplacian of enhanced edge local thresholding approach.

Key Features:

• Implicit Laplacian of Enhanced Edge algorithm: Implements an implicit Laplacian-enhanced edge local thresholding algorithm compatible with 2D and 3D data.
• Unguided local thresholding: Uses an unguided local thresholding approach to accommodate diverse filament shapes and brightness levels.
• Automated analysis: Performs automated analysis of cytoskeletal fluorescence images to produce quantitative outputs.
• Python library: Provides a Python library that implements the algorithm and analysis routines.
• Biologically interpretable indices: Computes indices measuring density, bundling, severing, branching, and directionality of cytoskeletal structures.
• Accuracy and robustness: Produces quantitatively descriptive data with improved accuracy, stability, and robustness and avoids biases introduced by 3D-to-2D projection.
• High-performance quantitative evaluation: Enables high-performance quantitative assessment of cytoskeletal status and organization.

Scientific Applications:

• Temporal and spatial cytoskeletal dynamics: Quantitative assessment of temporal and spatial dynamics of the eukaryotic cytoskeleton from fluorescence images.
• Cell signaling and trafficking studies: Investigation of cytoskeletal roles in cell signaling and trafficking relevant to immunity and disease in humans and plants.
• Comparative imaging analyses: Comparison of cytoskeletal organization across varied filament shapes and brightness levels while minimizing projection biases.
• Cell biology and disease mechanism research: Generation of descriptive quantitative data for studies of cell biology and disease mechanisms.

Methodology:

Implements an unguided 2D/3D-compatible local thresholding algorithm based on an implicit Laplacian of enhanced edge and is packaged as a Python library that performs automated image analysis and computes indices for density, bundling, severing, branching, and directionality while avoiding 3D-to-2D projection biases.