reconstructing fiber networks from image stacks

We have develloped 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 3x3 neighborhood. This optimization criterion makes our method free of user-defined parameters and the output exceptionally robust against imaging noise.

The effect of network reconstruction has been visualized in a video, which directly compares the raw data (right) and the corresponding reconstruction result (left) for a small sub-volume of a collagen stack.

The method is described in an arXiv preprint.

C++ Implementation and sample data set