Biological studies of dynamic processes in living cells often require accurate particle tracking as a first step toward quantitative analysis. Although many particle tracking methods have been developed for this purpose, they are typically based on prior assumptions about the particle dynamics, and/or they involve careful tuning of various algorithm parameters by the user for each application. This may make existing methods difficult to apply by non-expert users and to a broader range of tracking problems. Recent advances in deep-learning techniques hold great promise in eliminating these disadvantages, as they can learn how to optimally track particles from example data.

中文翻译：

---

粒子跟踪中数据关联的深度学习方法。

活细胞中动态过程的生物学研究通常需要精确的颗粒追踪，这是进行定量分析的第一步。尽管已经为此目的开发了许多粒子跟踪方法，但是它们通常基于关于粒子动力学的先前假设，和/或它们涉及用户针对每种应用对各种算法参数的仔细调整。这可能会使现有方法难以为非专业用户所用，并且难以解决更广泛的跟踪问题。深度学习技术的最新进展有望消除这些缺点，因为它们可以学习如何从示例数据中最佳跟踪粒子。