Victoria Research Laboratory, National ICT Australia (NICTA), Department of Computer Science and Software Engineering, University of Melbourne, VIC, Australia.
J Microsc. 2011 Nov;244(2):194-213. doi: 10.1111/j.1365-2818.2011.03529.x. Epub 2011 Sep 6.
Cell tracking is a key task in the high-throughput quantitative study of important biological processes, such as immune system regulation and neurogenesis. Variability in cell density and dynamics in different videos, hampers portability of existing trackers across videos. We address these potability challenges in order to develop a portable cell tracking algorithm. Our algorithm can handle noise in cell segmentation as well as divisions and deaths of cells. We also propose a parameter-free variation of our tracker. In the tracker, we employ a novel method for recovering the distribution of cell displacements. Further, we present a mathematically justified procedure for determining the gating distance in relation to tracking performance. For the range of real videos tested, our tracker correctly recovers on average 96% of cell moves, and outperforms an advanced probabilistic tracker when the cell detection quality is high. The scalability of our tracker was tested on synthetic videos with up to 200 cells per frame. For more challenging tracking conditions, we propose a novel semi-automated framework that can increase the ratio of correctly recovered tracks by 12%, through selective manual inspection of only 10% of all frames in a video.
细胞跟踪是高通量定量研究重要生物学过程（如免疫系统调节和神经发生）的关键任务。不同视频中细胞密度和动态的变化，妨碍了现有跟踪器在不同视频之间的可移植性。为了解决这些可移植性挑战，我们开发了一种可移植的细胞跟踪算法。我们的算法可以处理细胞分割中的噪声以及细胞的分裂和死亡。我们还提出了我们的跟踪器的无参数变化。在跟踪器中，我们采用了一种新的方法来恢复细胞位移的分布。此外，我们提出了一种数学上合理的方法来确定与跟踪性能相关的门控距离。对于测试的一系列真实视频，我们的跟踪器平均正确恢复了 96%的细胞移动，并且在细胞检测质量较高时优于先进的概率跟踪器。我们的跟踪器的可扩展性已在每帧多达 200 个细胞的合成视频上进行了测试。对于更具挑战性的跟踪条件，我们提出了一种新的半自动框架，通过选择性地仅检查视频中所有帧的 10%，可以将正确恢复的轨迹的比例提高 12%。
