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

Automating filament tracing in Cryo-Electron Microscopy(Cryo-EM) is essential for 3D helical reconstruction but challenged byintersecting topologies and extremely low Signal-to-Noise Ratios (SNR =σs2 /σn2 < 0.1 or -10 dB). Existing paradigms fail: pixel-wise segmenterssuffer from severe topological fracturing, box-based detectors face ghostcenter drift, sequential trackers derail due to error accumulation, and tra-ditional 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 percep-tion via a center-endpoint representation and an open-curve evolutionmodule to explicitly model non-cyclic connectivity. On simulated bench-marks, FPicker outperforms top baselines by over 40% relative gain inmean spatio-angular precision (mSAP) and reduces topological gap ratesby over 60% under extreme noise (−20 dB). By learning intrinsic phys-ical geometry rather than local texture, FPicker demonstrates strongpotential as a resilient geometric backbone. Its zero-shot performance onthe real-world EMPIAR dataset exhibits robust topological resistance,achieving a state-of-the-art 82.9% mSAP upon fine-tuning. Our resultsalso suggest modeling physical priors is a highly robust path towardbridging the sim-to-real gap in signal-starved scientific imaging. The codeis publicly available at: https://github.com/tomzhaosky/FPicker.