Department of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
Biomed Eng Online. 2019 Mar 1;18(1):20. doi: 10.1186/s12938-019-0638-1.
The research and analysis of cellular physiological properties has been an essential approach to studying some biological and biomedical problems. Temporal dynamics of cells therein are used as a quantifiable indicator of cellular response to extracellular cues and physiological stimuli.
This work presents a novel image-based framework to profile and model the cell dynamics in live-cell videos. In the framework, the cell dynamics between frames are represented as frame-level features from cell deformation and intracellular movement. On the one hand, shape context is introduced to enhance the robustness of measuring the deformation of cellular contours. On the other hand, we employ Scale-Invariant Feature Transform (SIFT) flow to simultaneously construct the complementary movement field and appearance change field for the cytoplasmic streaming. Then, time series modeling is performed on these frame-level features. Specifically, temporal feature aggregation is applied to capture the video-wide temporal evolution of cell dynamics.
Our results demonstrate that the proposed cell dynamic features can effectively capture the cell dynamics in videos. They also prove that the Movement Field and Appearance Change Field Feature (MFAFF) can more precisely model the cytoplasmic streaming. Besides, temporal aggregation of cell dynamic features brings a substantial absolute increase of classification performance.
Experimental results demonstrate that the proposed framework outperforms competing mainstreaming approaches on the aforementioned datasets. Thus, our method has potential for cell dynamics analysis in videos.
细胞生理特性的研究和分析一直是研究某些生物学和生物医学问题的重要方法。其中细胞的时间动态被用作细胞对外界刺激和生理刺激的反应的可量化指标。
本工作提出了一种新的基于图像的框架，用于分析和建模活细胞视频中的细胞动力学。在该框架中，细胞动力学在帧之间被表示为来自细胞变形和细胞内运动的帧级特征。一方面，引入形状上下文来增强测量细胞轮廓变形的鲁棒性。另一方面，我们采用尺度不变特征变换（SIFT）流来同时构建细胞质流动的互补运动场和外观变化场。然后，对这些帧级特征进行时间序列建模。具体来说，时间特征聚合用于捕获细胞动力学在视频中的全局时间演化。
我们的结果表明，所提出的细胞动态特征可以有效地捕捉视频中的细胞动力学。它们还证明了运动场和外观变化场特征（MFAFF）可以更精确地建模细胞质流动。此外，细胞动态特征的时间聚合带来了分类性能的实质性绝对提高。
实验结果表明，所提出的框架在上述数据集上优于竞争主流方法。因此，我们的方法在视频中的细胞动力学分析中具有潜力。
