Annasamudram Nagasoujanya, Zhao Jian, Oluwadare Olaitan, Prashanth Aashish, Makrogiannis Sokratis
Division of Physics, Engineering, Mathematics and Computer Science, Delaware State University, Dover, 19901, DE, USA.
Sci Rep. 2025 Apr 5;15(1):11717. doi: 10.1038/s41598-025-95993-w.
Monitoring and tracking of cell motion is a key component for understanding disease mechanisms and evaluating the effects of treatments. Time-lapse optical microscopy has been commonly employed for studying cell cycle phases. However, usual manual cell tracking is very time consuming and has poor reproducibility. Automated cell tracking techniques are challenged by variability of cell region intensity distributions and resolution limitations. In this work, we introduce a comprehensive cell segmentation and tracking methodology. A key contribution of this work is that it employs multi-scale space-time interest point detection and characterization for automatic scale selection and cell segmentation. Another contribution is the use of a neural network with class prototype balancing for detection of cell regions. This work also offers a structured mathematical framework that uses graphs for track generation and cell event detection. We evaluated cell segmentation, detection, and tracking performance of our method on time-lapse sequences of the Cell Tracking Challenge (CTC). We also compared our technique to top performing techniques from CTC. Performance evaluation results indicate that the proposed methodology is competitive with these techniques, and that it generalizes very well to diverse cell types and sizes, and multiple imaging techniques. The code of our method is publicly available on https://github.com/smakrogi/CSTQ_Pub/ , (release v.3.2).
监测和追踪细胞运动是理解疾病机制和评估治疗效果的关键组成部分。延时光学显微镜已普遍用于研究细胞周期阶段。然而，常规的手动细胞追踪非常耗时且重现性差。自动细胞追踪技术面临细胞区域强度分布的可变性和分辨率限制的挑战。在这项工作中，我们介绍了一种全面的细胞分割和追踪方法。这项工作的一个关键贡献在于它采用多尺度时空兴趣点检测和表征来进行自动尺度选择和细胞分割。另一个贡献是使用具有类原型平衡的神经网络来检测细胞区域。这项工作还提供了一个结构化的数学框架，该框架使用图来生成轨迹和检测细胞事件。我们在细胞追踪挑战赛（CTC）的延时序列上评估了我们方法的细胞分割、检测和追踪性能。我们还将我们的技术与 CTC 中表现最佳的技术进行了比较。性能评估结果表明，所提出的方法与这些技术具有竞争力，并且能够很好地推广到不同的细胞类型和大小以及多种成像技术。我们方法的代码可在 https://github.com/smakrogi/CSTQ_Pub/ （版本 v.3.2）上公开获取。
