Reconstructing fiber networks from confocal image stacks

TL;DR

This paper introduces a fast, scale-invariant method for reconstructing 3D biopolymer networks from confocal images using adaptive template matching, which is robust to noise and parameter-free.

Contribution

A novel, automated template matching algorithm that performs binarization and skeletonization of biopolymer networks from 3D images without user-defined parameters.

Findings

Method accurately reconstructs fiber networks from noisy confocal images.

Algorithm is scale-invariant and adapts to different data sets.

Reconstructed networks obey universal voxel neighborhood properties.

Abstract

We present a numerically efficient method to reconstruct a disordered network of thin biopolymers, such as collagen gels, from three-dimensional (3D) image stacks recorded with a confocal microscope. Our method is based on a template matching algorithm that simultaneously performs a binarization and skeletonization of the network. The size and intensity pattern of the template is automatically adapted to the input data so that the method is scale invariant and generic. Furthermore, the template matching threshold is iteratively optimized to ensure that the final skeletonized network obeys a universal property of voxelized random line networks, namely, solid-phase voxels have most likely three solid-phase neighbors in a $3\times3$ neighborhood. This optimization criterion makes our method free of user-defined parameters and the output exceptionally robust against imaging noise.