用自学习视觉特征精准拼接间歇性粒子轨迹
Tracking Intermittent Particles with Self-Learned Visual Features
- 通过自监督学习提取粒子视觉特征，实现跨断点匹配
- 在水螅神经元数据上使拼接错误率降低50%
- 适合处理荧光成像中频繁遮挡的粒子追踪任务
在时间序列荧光成像中，单粒子追踪是监测目标动态并获取生物过程信息的强大工具。然而，被追踪粒子常因遮挡或间歇性可见导致跟踪算法生成多个片段轨迹。本文提出一种自监督视觉特征学习方法，结合视觉与位置距离，实现对同一粒子轨迹片段的鲁棒拼接。在水螅（Hydra vulgaris）神经元的时间序列荧光图像上验证了该框架的有效性，结果表明拼接精度高，相比先前算法在同一数据上的错误率降低了一半。
原文摘要 · Abstract (English)
In time-lapse fluorescence imaging, single-particle-tracking is a powerful tool to monitor the dynamics of objects of interest, and extract information about biological processes. However, tracked particles can be subject to occlusion and intermittent detectability. When these phenomena persist over a few frames, tracking algorithms tend to produce multiple tracklets for the same particle. In this work, we introduce self-supervised learning of visual features to compare tracked particles, and we exploit both visual and positional distances to robustly stitch tracklets representing the same particle. We demonstrate the performance of our stitching framework on time-lapse fluorescence sequences of Hydra vulgaris neurons. Results show high stitching precision, and reduction of errors made by previous algorithms on the same data by a factor of two.
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