FPicker: Topology-Guided Evolution for Filament Tracing in Low-SNR Microscopy

• URL: http://arxiv.org/abs/2609.08305v1
• Date: Tue, 08 Sep 2026 06:25:44 GMT
• Title: FPicker: Topology-Guided Evolution for Filament Tracing in Low-SNR Microscopy
• Abstract summary: FPicker is a topology-guided framework for 3D filament tracing in Cryo-Electron Microscopy.<n>It unifies perception via a center-endpoint representation and an open-curve evolution module to explicitly model non-cyclic connectivity.<n>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.
• Score: 14.980713248913732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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