Source-linked AI summary
FPicker: Topology-Guided Evolution for Filament Tracing in Low-SNR Microscopy
Tingyin Zhao, Mingtao Huang, Yuan Shen
TL;DR
Cryo-EM filament tracing must recover flexible, intersecting structures from extremely low-SNR images, while existing paradigms suffer topological and geometric failures. FPicker combines center-endpoint perception with open-curve evolution and curriculum-based training to model filament topology explicitly. It achieves strong extreme-noise performance and state-of-the-art fine-tuned results on real-world EMPIAR data, while retaining scope limitations around branching and occasional over-merging.
Problem
Cryo-EM filament tracing is needed for helical parameter estimation and 3D reconstruction but is difficult under intersecting topologies and extremely low SNR.
Method
FPicker uses a center-endpoint representation, open-boundary graph evolution, and a bridge curriculum to learn filament geometry under extreme noise.
Results
76.5% mSAP and a 9.8% gap rate under Extreme noise (-20 dB), with 82.9% fine-tuned mSAP on Custom-EMPIAR.
Takeaways & Limitations
The results support modeling intrinsic physical priors as a robust geometric backbone for signal-limited scientific imaging and sim-to-real transfer.
Takeaways & Limitations
FPicker does not provide unified modeling of true branching and can occasionally over-merge aligned filament fragments.
Abstract
from arXiv · showhide
Automating filament tracing in Cryo-Electron Microscopy (Cryo-EM) is essential for 3D helical reconstruction but challenged by intersecting topologies and extremely low Signal-to-Noise Ratios ($\text{SNR} = σ_s^2/σ_n^2$ < 0.1 or -10 dB). Existing paradigms fail: pixel-wise segmenters suffer from severe topological fracturing, box-based detectors face ghost center drift, sequential trackers derail due to error accumulation, and traditional active contours collapse under artificial closed-curve constraints. To resolve these bottlenecks, we present FPicker, the first topology-guided framework reconciling these incompatibilities. It unifies perception via a center-endpoint representation and an open-curve evolution module to explicitly model non-cyclic connectivity. On simulated benchmarks, FPicker outperforms top baselines by over $40\%$ relative gain in mean spatio-angular precision (mSAP) and reduces topological gap rates by over $60\%$ under extreme noise ($-20\text{ dB}$). By learning intrinsic physical geometry rather than local texture, FPicker demonstrates strong potential as a resilient geometric backbone. Its zero-shot performance on the real-world EMPIAR dataset exhibits robust topological resistance, achieving a state-of-the-art 82.9\% mSAP upon fine-tuning. Our results also suggest modeling physical priors is a highly robust path toward bridging the sim-to-real gap in signal-starved scientific imaging. The code is publicly available at: https://github.com/tomzhaosky/FPicker.
1 Introduction
Cryo-EM filament tracing is difficult because flexible, intersecting filaments must be recovered from extremely low-SNR images, where existing geometric paradigms break down. FPicker addresses this gap with center-endpoint perception, open-curve evolution, and training strategies designed for resilient geometric modeling.
- Motivation: Cryo-EM filament picking remains a laborious bottleneck for helical parameter estimation and subsequent 3D helical reconstruction.
- Motivation: At SNR < 0.1 or -10 dB, target signal is often locally indistinguishable from background noise.
- Existing limitations: Box-based detectors suffer ghost-center drift, while segmentation models can fracture topology when tracing flexible intersecting filaments.
- Existing limitations: Sequential trackers accumulate errors into background, and cyclic active contours pull open-filament tips together.
- FPicker: FPicker replaces box priors with a center-endpoint representation and uses open-boundary graph evolution to model non-cyclic filament topology.
- FPicker: A bridge curriculum decouples local deformation-physics learning from global localization variance during extreme-noise end-to-end training.
- Results: Optimizing within Cryo-Sim yields effective zero-shot transfer and state-of-the-art fine-tuning performance on real-world EMPIAR micrographs.
2 Related Works
Related filament-tracing methods use box linking, segmentation followed by skeletonization, or explicit topology modeling, but each has geometric weaknesses in low-SNR intersecting structures. These weaknesses motivate a representation that preserves instances and continuity without relying on ambiguous local evidence.
- Box-based detection: Box-based detectors represent flexible 1D curves with 2D boxes, causing background-heavy proposals, anchor-size tuning, and ghost-center drift.
- Segmentation: Segment-then-skeletonize methods extract contours but can merge distinct filament instances at 2D projection crossovers.
- Topology modeling: Sequential trackers extend trajectories from local seeds but can irreversibly derail into background through Markovian error accumulation under extreme noise.
3 Methods
FPicker uses a coarse-to-fine, two-stage pipeline that generates center-endpoint skeletal priors and refines them as open curves. Auxiliary geometric supervision, global-local feature fusion, and bridge curriculum training support topology-preserving deformation in extreme noise.
- Overview: FPicker maps each Cryo-EM micrograph to open skeletal trajectories through a shared feature extractor, proposal generation, and topological refinement.The architecture is end-to-end trainable and follows a coarse-to-fine design.
- Proposal Generation: Stage I detects topological centroids and endpoint displacements, interpolating them into linear skeletal priors that anchor subsequent curve evolution.Centroids are constrained to filament arc-length medians, while the priors define a rotation-aware capture range.
- Open-Curve Evolution: Stage II samples vertex features along each prior and iteratively deforms an open graph, preserving axial integrity without closed-curve tip retraction.The graph connects each internal vertex only to its immediate neighbors, and open-boundary aggregation sets tip tension to zero.
- Open-Curve Evolution: Local OBC and global feature streams fuse to guide deformation, with data-dependent global descriptors broadcasting high-confidence structural cues to low-signal vertices.The global stream aggregates salient signals while auxiliary geometric regularization helps resist high-frequency noise.
- Training Strategy: The bridge curriculum gradually replaces ground-truth initialization with predicted priors, decoupling local deformation learning from early localization variance.Ground-truth injection is used first, then annealed toward autonomous inference as the detector stabilizes.
- Loss Function: The evolution loss combines vertex fitting with edge-length uniformity to align curves with skeleton topology while maintaining physically plausible vertex spacing.This regularization is intended to prevent vertex clustering and bridge fragmented signals in extreme low-SNR conditions.
4 Experiments
FPicker is evaluated across simulated noise regimes, real-world transfer, data efficiency, and component ablations. It consistently addresses ghost-center collapse, topological fracturing, and endpoint shrinking while retaining strong performance under extreme noise.
- Experimental setup: Cryo-Sim contains 20,000 simulated micrographs spanning High (0.1, -10 dB), Medium (0.05, -13 dB), and Extreme (0.01, -20 dB) noise regimes.Custom-EMPIAR contains 500 manually annotated real-world micrographs.
- Experimental setup: FPicker is benchmarked against box-based detectors, pixel-wise segmenters, and closed active contours using mSAP, F1-score, gap rate, clDice, fp-clDice, and penalized tangent error.mSAP evaluates spatial Chamfer distance under an angular tolerance of Δθ < 15°.
- Comparison with state-of-the-art: At Extreme noise (-20 dB), YOLOv8 and crYOLO fall to 28.5% and 25.4% mSAP, respectively, while YOLOv8 reaches a 45.2% gap rate.The results are attributed to bounding-box centers drifting into background signal.
- Comparison with state-of-the-art: FPicker achieves a 9.8% gap rate and 20.5° angle error under Extreme noise, indicating that open-curve evolution reduces endpoint tension while preserving continuity.Segmentation methods show high gap rates, and Deep Snake reaches 26.2° angle error at Medium SNR.
- Sim-to-real transfer: FPicker reaches 82.9% mSAP after fine-tuning on Custom-EMPIAR, following zero-shot performance of 42.4% clDice and 28.6% mSAP.SAM 2 reaches 1.5% zero-shot mSAP, while established detectors reach 25.4% and 26.2% mSAP in the cited comparison.
- Data efficiency: Fine-tuning FPicker with 10 images raises mSAP from 28.6% to 55.4%, and 50 images raise it to 70.1%.The reported adaptation uses sparse real-world annotations after Cryo-Sim training.
- Ablation studies: Ablations show that the center-endpoint representation raises EMPIAR mSAP to 52.5%, open-curve evolution lowers P-Ang from 45.2° to 19.4° at Medium SNR, and auxiliary fields raise Extreme-noise mSAP from 35.6% to 45.2%.The bridge curriculum further raises Extreme-noise mSAP to 76.5% and EMPIAR mSAP to 82.9%.
- Iteration refinement: Performance rises from 78.5% mSAP at T = 1 to 91.1% at T = 3, while inference speed remains between 38.3 and 36.9 FPS.The iteration analysis reports saturation around three deformation iterations.
5 Limitations
FPicker remains bounded by its current topology and continuity assumptions. The paper identifies limitations in branching structures and occasional over-merging of aligned fragments.
- Topology scope: FPicker does not jointly model branches such as Y-junctions, limiting its direct extension to vascular or neural branching.The authors state that true molecular branching is absent from the target Cryo-EM filaments and leave dynamic node degrees for future work.
- Continuity trade-off: FPicker occasionally over-merges aligned fragments despite a reported Over-Merging Rate of 7.96%.Explicitly repelling competing tips without sacrificing noise resistance remains unresolved.
6 Conclusions
FPicker addresses filament-tracing incompatibilities by combining center-endpoint representation with open-boundary graph evolution. Under extreme noise, it achieves strong simulated performance and state-of-the-art fine-tuned performance on Custom-EMPIAR.
- Conclusions: FPicker replaces rigid box priors with center-endpoint representation and unites holistic graph evolution with strictly open-boundary constraints.This design targets local tracking derailment and cyclic shrinking in filament tracing.
- Conclusions: 76.5% mSAP and a 9.8% gap rate are achieved under Extreme noise (-20 dB).These results are reported alongside prevention of ghost center collapse and endpoint shrinking.
- Conclusions: 82.9% fine-tuned mSAP is achieved on Custom-EMPIAR after training on Cryo-Sim.The reported training strategy also delivers effective zero-shot transfer to real-world data.
- Conclusions: Modeling intrinsic physical priors is presented as more robust than brute-force data scaling for signal-limited scientific imaging.The conclusion frames FPicker as a resilient geometric backbone for these regimes.