用稀疏点击修正轨迹，实现显微镜视频中专家级点追踪
Motion-guided sparse correction enables expert-quality point tracking across diverse microscopy regimes
- 用户仅需点击起点，系统自动预测轨迹并只在漂移处干预
- 相比全量标注减少3至25倍手动点击，精度媲美人工精标
- 适合需要快速生成高质量追踪数据的生物动态研究者
在显微镜视频中追踪非典型生物系统的动态仍是一大挑战。传统与基于学习的追踪方法均依赖专家标注数据进行评估和调优，但全面的人工标注难以扩展到最需要这些工具的视频场景。我们开发了RIPPLE（点位估计的修正插值平台），将标注重构为稀疏修正：用户点击一个起始点，RIPPLE生成完整轨迹，仅在轨迹漂移时介入修正。我们在五个来自实验室的高挑战性显微镜数据集上测试了RIPPLE，包括四种来自透明水母Clytia hemisphaerica的视频和一种追踪快速运动精子的特征点。RIPPLE在所有数据集中达到与全量人工标注相当的精度，同时将手动点击次数减少了3至25倍。RIPPLE填补了人工标注与完全自动化追踪之间的空白，使生物动态的即时量化、方法基准测试以及未来自动化显微追踪器所需的金标准数据生产成为可能。
原文摘要 · Abstract (English)
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.
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