ReLink: Reliable Deep Learning for Microscopic Motion Tracking

Single-particle tracking (SPT) serves as a fundamental technique for deciphering complex microscopic dynamics in biological and physical systems by resolving trajectories of individual particles. However, linking localizations into trajectories remains challenging in realistic experiments, where crowding, blinking, and localization errors lead to linking ambiguity. Both statistical and deep-learning approaches have been explored in these conditions, yet reliable link selection that generalizes across varying data distributions remains elusive. Here, we propose ReLink, a general deep-learning framework for reliable trajectory linking in SPT. By integrating physical priors into geometric representations and leveraging confidence-based adaptive filtering at inference, ReLink enables automated selection of trustworthy linkages, thereby preserving the fidelity of downstream trajectory-based analyses. Across challenging simulated scenarios spanning broad experimental conditions and motion regimes, ReLink consistently outperforms representative statistical and deep learning-based trajectory linking methods in both edge-level and trajectory-level evaluation metrics, leading to markedly improved downstream inference on public benchmarks. We further validate ReLink on diverse real-world experiments, from free diffusion systems to crowded intracellular environments, demonstrating its practical significance for quantitative interpretation of microscopic dynamics and its promise for next-generation trustworthy and automated SPT.