arxiv: 2604.18230 · v1 · submitted 2026-04-20 · 🧬 q-bio.QM · cond-mat.mtrl-sci· cond-mat.soft

Recognition: unknown

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

Behrooz Fereidoonnezhad, Gijsje H. Koenderink, Ir\`ene Nagle, Mathias Peirlinck, Risa Togo, Sara Cardona

Pith reviewed 2026-05-10 03:25 UTC · model grok-4.3

classification 🧬 q-bio.QM cond-mat.mtrl-scicond-mat.soft

keywords fiber network reconstructiontopology preservation3D microscopycollagen gelsimage segmentationbiological fibrous networksconnectivity analysis

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The pith

ToFiE reconstructs connected three-dimensional fiber networks from high-resolution microscopy images by preserving topology instead of using intensity thresholds.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents ToFiE as a practical method to turn raw 3D microscope images of dense fiber networks into accurate digital reconstructions that keep all the original connections between fibers. Biological fiber networks, such as collagen in tissues, behave mechanically according to how their fibers link up, yet existing image processing tools commonly break those links by relying on brightness cutoffs. ToFiE adds topology-aware processing steps that detect and maintain junctions even in noisy or heterogeneous images. Validation on synthetic networks with known ground truth and on real confocal images of collagen gels shows that the extracted networks retain connectivity across varying densities and signal qualities. This opens the door to direct quantitative links between imaged network structure and measured mechanical properties in fibrous biological materials.

Core claim

ToFiE is an open-source workflow that reconstructs dense and heterogeneous biological fiber networks from 3D microscopy images while preserving their three-dimensional connectivity. It avoids the fragmentation produced by intensity-based thresholding through dedicated topology-preserving operations, and it has been shown to recover accurate network graphs on both synthetic test images with controlled noise levels and real confocal datasets of collagen gels with different microstructures.

What carries the argument

ToFiE, a semi-automated workflow that applies topology-aware fiber extraction steps to 3D image volumes so that detected fibers remain joined at their natural junctions.

If this is right

Reconstructed networks can be used as direct inputs for mechanical simulations that predict stiffness or failure from measured connectivity.
Quantitative comparison of fiber junction density and path lengths becomes possible across different collagen gel preparations.
The same workflow applies to other fibrous systems such as fibrin clots or cellulose networks without requiring new intensity tuning.
Open-source availability allows labs to extract mechanically relevant topology metrics from their own confocal or fluorescence datasets.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The method could be combined with time-lapse imaging to track how fiber connectivity changes during tissue remodeling.
Accurate topology data from ToFiE would let researchers test theoretical predictions that link network architecture to bulk mechanics more cleanly than before.
Extension to other imaging modalities such as light-sheet microscopy would require only minor adjustments to the preprocessing stage.

Load-bearing premise

The topology-preserving processing steps continue to identify true fiber connections correctly even when real images contain intensity fluctuations, overlapping fibers, and imaging artifacts that exceed those present in the synthetic validation sets.

What would settle it

Application of ToFiE to real collagen gel images followed by direct comparison to manual tracing would show artificial breaks at junctions that do not exist in the original data.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ToFiE gives a semi-automated workflow for pulling connected 3D fiber networks out of microscopy images, but the topology-preservation claim rests on synthetic tests and qualitative demos rather than quantitative checks on real data.

read the letter

The main takeaway is that ToFiE packages segmentation, skeletonization, and reconnection steps into an open-source pipeline meant to keep 3D connectivity intact when extracting dense biological fiber networks. That addresses a practical need in quantitative biology, where models of collagen, fibrin, or plant cell walls depend on accurate topology for mechanical predictions. The synthetic validation covers varying topologies and SNR levels, which at least shows the steps can work under controlled conditions, and the collagen-gel examples illustrate how it might look on confocal data. Making the code available is a clear positive for anyone who wants to try it on their own images. What is new is the integrated, semi-automated framing tailored to heterogeneous fibers rather than generic image-processing tools used separately. The soft spot is the missing quantitative evidence on real images. No error rates for false merges or splits at junctions are reported, and there is no independent ground truth for the actual network topology in the experimental collagen data. Real microscopy brings intensity gradients, overlaps, and artifacts that the synthetic sets do not fully capture, so it is unclear whether the reconnection heuristics actually reduce fragmentation compared with simple thresholding. Without those metrics or side-by-side comparisons, the central advantage stays unproven. This paper is for researchers in bioimaging and soft-matter mechanics who need network graphs from 3D stacks and are willing to test the workflow themselves. A reading group on quantitative biology or image analysis would find the problem statement and the demo useful, even if the results section needs strengthening. It deserves peer review because the idea targets a real bottleneck and the open-source release lowers the barrier to checking the implementation. Referees would likely ask for connectivity error metrics on real data and perhaps more varied test cases, but the work is coherent enough to warrant that step rather than a desk reject.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces ToFiE, an open-source topology-aware fiber extraction workflow for 3D reconstruction of dense and heterogeneous biological fiber networks from high-resolution microscopy images. It claims to preserve network connectivity better than intensity-thresholding approaches, with validation on synthetic fluorescence images featuring controlled variations in topology and SNR, followed by demonstration on confocal images of collagen gels with varying microstructures.

Significance. If the topology-preserving steps prove robust, ToFiE would supply a practical semi-automated, open-source framework for extracting mechanically relevant 3D network connectivity from imaging data of fibrous biomaterials such as collagen. The explicit validation on synthetic data with varying topologies and SNR levels, together with the release of the workflow, are concrete strengths that could facilitate adoption in quantitative biology.

major comments (2)

[Synthetic validation] Synthetic validation (as described in the abstract and results): The paper reports validation on synthetic images with varying topologies and SNR but supplies no quantitative metrics such as connectivity error rates, junction detection precision, false-merge/split counts, or direct numerical comparisons to intensity-thresholding baselines; this is load-bearing for the central claim of topology preservation.
[Experimental demonstration] Experimental demonstration on collagen gels (results section): Reconstructions are presented qualitatively via visualizations, yet no independent ground-truth topology or quantitative measures of residual fragmentation under real imaging conditions (local intensity gradients, overlaps, partial-volume effects) are provided; this leaves the accuracy of the skeletonization, junction detection, and reconnection heuristics unverified on biological data.

minor comments (2)

[Abstract] The abstract would benefit from a concise statement of the specific quantitative improvements (if any) over prior methods and the exact number of synthetic test cases used.
[Figures] Figure captions and legends should explicitly distinguish synthetic from experimental data panels and include scale bars for all 3D renderings.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the detailed and constructive review. We address each major comment point by point below, indicating the revisions we will implement to strengthen the manuscript.

read point-by-point responses

Referee: Synthetic validation (as described in the abstract and results): The paper reports validation on synthetic images with varying topologies and SNR but supplies no quantitative metrics such as connectivity error rates, junction detection precision, false-merge/split counts, or direct numerical comparisons to intensity-thresholding baselines; this is load-bearing for the central claim of topology preservation.

Authors: We agree that quantitative metrics are necessary to rigorously support the topology-preservation claims. In the revised manuscript we will add direct numerical comparisons on the synthetic datasets, reporting connectivity error rates, junction detection precision and recall, false-merge and false-split counts, and performance against intensity-thresholding baselines across the tested SNR and topology variations. revision: yes
Referee: Experimental demonstration on collagen gels (results section): Reconstructions are presented qualitatively via visualizations, yet no independent ground-truth topology or quantitative measures of residual fragmentation under real imaging conditions (local intensity gradients, overlaps, partial-volume effects) are provided; this leaves the accuracy of the skeletonization, junction detection, and reconnection heuristics unverified on biological data.

Authors: We acknowledge that the collagen-gel results are qualitative demonstrations rather than quantitative validations, because independent ground-truth topology cannot be obtained for these real images. In revision we will explicitly state this limitation in the results and discussion sections, clarify that the synthetic experiments provide the controlled quantitative evidence, and note how the synthetic conditions were designed to emulate real imaging artifacts such as intensity gradients and partial-volume effects. revision: partial

standing simulated objections not resolved

Independent ground-truth topology is unavailable for the experimental collagen-gel images, so direct quantitative verification of skeletonization and reconnection accuracy on biological data cannot be provided.

Circularity Check

0 steps flagged

No circularity detected in derivation chain

full rationale

The paper describes an algorithmic workflow (ToFiE) for 3D fiber network reconstruction from microscopy images, with validation on independent synthetic datasets (controlled topologies and SNR) followed by application to real collagen gel images. No equations, first-principles derivations, fitted parameters renamed as predictions, or self-citation chains are present that would make any output definitionally equivalent to its inputs. The topology-preservation claim rests on empirical validation rather than construction from the method itself.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The method rests on standard assumptions from image processing and graph theory for fiber segmentation and connectivity; no new physical entities or ad-hoc fitted constants are introduced in the abstract.

axioms (1)

domain assumption Fiber networks can be represented as graphs whose topology is recoverable from 3D intensity data without fragmentation
Invoked implicitly when claiming that current thresholding methods distort junctions and that ToFiE preserves connectivity.

pith-pipeline@v0.9.0 · 5525 in / 1175 out tokens · 48261 ms · 2026-05-10T03:25:41.359803+00:00 · methodology

discussion (0)

Reference graph

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and collagen networks (Figure3). For more details on the parameters in steps 5 and 6, we refer the readers to the DisPerSe manual by Sousbie [24]. 23 afrequency frequency b Figure S2:Dependence of the recall score on the tolerance radius r for a representative synthetic network.The recall score for nodes with connectivityk n =3 (triangle) ork n =4 (square...