Abstract
Arabidopsis is a widely used model plant to gain basic knowledge on plant physiology and development. Live imaging is an important technique to visualize and quantify elemental processes in plant development. To uncover novel theories underlying plant growth and cell division, accurate cell tracking on live imaging is of utmost importance. The commonly used cell tracking software, TrackMate, adopts tracking-by-detection fashion, which applies Laplacian of Gaussian (LoG) for blob detection, and Linear Assignment Problem (LAP) tracker for tracking. However, they do not perform sufficiently when cells are densely arranged. To alleviate the problems mentioned above, we propose an accurate tracking method based on Genetic algorithm (GA) using knowledge of Arabidopsis root cellular patterns and spatial relationship among volumes. Our method can be described as a coarse-to-fine method, in which we first conducted relatively easy line-level tracking of cell nuclei, then performed complicated nuclear tracking based on known linear arrangement of cell files and their spatial relationship between nuclei. Our method has been evaluated on a long-time live imaging dataset of Arabidopsis root tips, and with minor manual rectification, it accurately tracks nuclei. To the best of our knowledge, this research represents the first successful attempt to address a long-standing problem in the field of time-lapse microscopy in the root meristem by proposing an accurate tracking method for Arabidopsis root nuclei.
Abstract (translated)
拟南芥是一种广泛用于获取植物生理和发育基本知识的模型植物。活体成像是可视化并量化植物发育过程中基本元素过程的重要技术。为了揭示新的理论，阐明植物生长和细胞分裂背后的机制，对活体成像中的精确细胞追踪至关重要。目前常用的细胞追踪软件TrackMate采用基于检测的追踪方法，通过高斯拉普拉斯算子（LoG）进行斑点检测，并使用线性分配问题（LAP）跟踪器来进行追踪。然而，在细胞密集排列的情况下，这种方法的效果并不理想。 为了解决上述问题，我们提出了一种基于遗传算法（GA）并结合拟南芥根部细胞模式和体积间空间关系知识的精确追踪方法。我们的方法可以描述为从粗到细的过程：首先进行相对简单的线性级别的细胞核追踪，然后根据已知的细胞文件线性排列及其细胞核之间的空间关系来进行复杂的核追踪。 我们在拟南芥根尖长时间活体成像数据集上对这种方法进行了评估，并在经过少量人工修正后，该方法能够精确地追踪细胞核。据我们所知，这项研究是首次成功尝试解决时序显微镜技术中长期存在的问题——即为拟南芥根部核提供准确的跟踪方法，这在分生组织领域尤为突出。
URL
https://arxiv.org/abs/2504.12676
