这篇文章介绍了 OrganoidTracker 2.0，这是一种用于延时成像细胞追踪的先进算法，它结合了 神经网络 和 统计物理学 的概念，以确定细胞轨迹并提供准确的错误概率。不同于现有方法只能输出单个追踪方案且缺乏量化不确定性的统计基础，OrganoidTracker 2.0 能够为轨迹中的每一步分配错误概率，这些概率可作为分析细胞周期和谱系树等追踪特征的P值。这种新方法通过将人工校正限制在极少数低置信度步骤，极大地提高了追踪分析的速度，甚至允许通过保留高置信度轨迹片段进行 全自动化分析，从而促进了基于细胞动力学的类器官高通量筛选和透明的科学报告。该方法在肠道类器官、小鼠胚泡和秀丽隐杆线虫胚胎等多种系统上都表现出卓越的追踪性能。References: Betjes M A, Kok R N U, Tans S J, et al. Cell tracking with accurate error prediction[J]. Nature Methods, 2025: 1-11.
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