Abstract
Tracking cells in time-lapse videos is an essential technique for monitoring cell population dynamics at a single-cell level. Current methods for cell tracking are developed on videos with mostly single, constant signals and do not detect pivotal events such as cell death. Here, we present TransientTrack, a deep learning-based framework for cell tracking in multi-channel microscopy video data with transient fluorescent signals that fluctuate over time following processes such as the circadian rhythm of cells. By identifying key cellular events - mitosis (cell division) and apoptosis (cell death) our method allows us to build complete trajectories, including cell lineage information. TransientTrack is lightweight and performs matching on cell detection embeddings directly, without the need for quantification of tracking-specific cell features. Furthermore, our approach integrates Transformer Networks, multi-stage matching using all detection boxes, and the interpolation of missing tracklets with the Kalman Filter. This unified framework achieves strong performance across diverse conditions, effectively tracking cells and capturing cell division and death. We demonstrate the use of TransientTrack in an analysis of the efficacy of a chemotherapeutic drug at a single-cell level. The proposed framework could further advance quantitative studies of cancer cell dynamics, enabling detailed characterization of treatment response and resistance mechanisms. The code is available at this https URL.
Abstract (translated)
在时间推移视频中追踪细胞是一种监测单个细胞水平上细胞群体动态的重要技术。目前的细胞追踪方法主要针对信号单一且恒定的视频，无法检测到如细胞死亡这样的关键事件。在这里，我们提出了TransientTrack，这是一种基于深度学习的框架，用于多通道显微镜视频数据中的细胞追踪，该框架能够识别随时间波动的瞬时荧光信号（例如遵循细胞昼夜节律的过程）。通过识别关键的细胞事件——有丝分裂（细胞分裂）和凋亡（细胞死亡），我们的方法允许构建包括谱系信息在内的完整轨迹。TransientTrack轻量级且直接在检测嵌入上进行匹配，无需量化特定于追踪的细胞特征。此外，我们的方法整合了Transformer网络、多阶段匹配以及使用卡尔曼滤波器插补缺失片段的技术。这种统一框架在各种条件下均表现出色，能够有效跟踪细胞并捕捉到细胞分裂和死亡事件。 我们展示了TransientTrack在单细胞水平上分析化疗药物疗效的应用示例。所提出的框架可以进一步推进癌症细胞动力学的定量研究，使详细表征治疗反应及抗性机制成为可能。代码可在以下网址获取：[提供链接]
URL
https://arxiv.org/abs/2512.01885
