Motion-guided sparse correction enables expert-quality point tracking across diverse microscopy regimes

2026-05-27

AI Summary

This work addresses the high cost and limited scalability of fully manual annotation in tracking dynamic behaviors of atypical biological systems in microscopy videos. To overcome these challenges, the authors propose RIPPLE, a novel platform that introduces a “sparse correction” paradigm: users provide only an initial click and perform minimal corrections when trajectory drift occurs, enabling the system to generate full trajectories with expert-level accuracy. RIPPLE integrates motion-guided trajectory interpolation with an interactive feedback mechanism, establishing an efficient intermediate tier between fully automatic tracking and exhaustive manual labeling. Experiments across five complex microscopy datasets demonstrate that RIPPLE achieves annotation quality on par with full manual labeling while reducing user clicks by 3–25×, thereby facilitating quantitative analysis of biological dynamics, algorithm benchmarking, and generation of gold-standard datasets.

Abstract

Tracking the dynamics of non-canonical biological systems in microscopy videos remains a persistent challenge. Both classical and learning-based trackers depend on expert-reviewed data to be evaluated and adapted, yet exhaustive manual annotation rarely scales to the videos where these tools are needed most. We developed RIPPLE (Refinement Interpolation Platform for Point Location Estimation), which recasts annotation as sparse correction: a user clicks a starting point, RIPPLE proposes a full trajectory, and the user intervenes only where the trajectory drifts. We tested RIPPLE on five challenging microscopy datasets from our laboratories, four from the transparent jellyfish Clytia hemisphaerica and one tracking landmarks on rapidly moving sperm. Across these, RIPPLE matched the quality of exhaustive manual annotation while reducing manual clicks by 3 to 25 times across datasets. RIPPLE thereby fills a missing layer between manual annotation and fully automated tracking, enabling immediate quantification of biological dynamics, method benchmarking, and the production of the gold-standard data needed to adapt future automated microscopy trackers.

Problem

• point tracking
• microscopy
• manual annotation
• biological dynamics
• sparse correction

Innovation

• sparse correction
• motion-guided tracking
• point tracking
• microscopy video analysis
• human-in-the-loop annotation