ToFiE, a Topology-aware Fiber Extraction workflow for 3D reconstruction of dense and heterogeneous biological fiber networks from microscopy images

TL;DR

ToFiE is an open-source workflow designed to accurately reconstruct 3D fibrous networks from microscopy images, preserving network topology and connectivity for biological and material research.

Contribution

It introduces a topology-aware fiber extraction method that outperforms traditional intensity-based segmentation in preserving network connectivity.

Findings

Successfully validated on synthetic fiber networks with varying topologies.

Effectively reconstructed collagen gel networks with diverse microstructures.

Established as a practical tool for extracting mechanically relevant network data.

Abstract

Fibrous networks are ubiquitous structural components in biology, spanning cellulose in plant cell walls, fibrin in blood clots, and collagen in the extracellular matrix of animal tissues. Theoretical models predict that network connectivity critically influences their mechanical behavior. However, accurately reconstructing network topology from 3D image data remains a major challenge as current segmentation methods are not designed to preserve network topology and often rely on intensity-based thresholding, which can fragment fibers and distort junction connectivity. Here, we introduce ToFiE, an open-source topology-aware fiber extraction workflow for reconstructing dense and heterogeneous fibrous networks from high resolution microscopy images while preserving connectivity in three dimensions. We validate ToFiE using synthetic fluorescence microscopy images of fiber networks with varying topologies and signal-to-noise ratios. We further demonstrate its performance by reconstructing the fiber networks of a library of collagen gels with various microstructures, imaged using confocal fluorescence microscopy. Altogether, the results establish ToFiE as a practical semi-automated framework for extracting mechanically relevant network information from imaging data across a broad range of fibrous materials.