基于视频特征聚合的细胞形变动态建模

提出了一种基于图像处理架构对活细胞视频中细胞动态进行建模的方法.在这个架构下，视频中两帧间的细胞动态被表示为细胞轮廓形变和细胞内部运动的动态特征.其中，前者细胞轮廓形变利用形状上下文进行度量，而后者细胞内部运动则通过尺度不变特征变换（SIFT）流进行建模.为了更好的刻画细胞质流动，基于SIFT流的细胞运动场进一步构建细胞外观变化场.在获得上述帧级细胞动态特征之后，引入时间序列建模的方法来生成视频级的细胞动态特征.具体地，基于紧凑编码的时序特征聚合方法可以捕获整个视频中的细胞动态演变过程.活细胞视频数据库被建立以用于验证提出方法的有效性，实验结果表明提出方法对于细胞形变动态度量和分类的性能优于其它主流方法.

Analyzing Temporal Dynamics of Cell Deformation with Video Feature Aggregation

A modeling method was proposed based on a novel image-based framework to profile and model the cell dynamics in live-cell videos. In the framework, the cell dynamics between frames were represented as frame-level features about cell deformation and intracellular movement. The cell deformation was captured by the shape context, while the intracellular movement was modeled with SIFT (Scale-Invariant Feature Transform) flow. In order to completely evaluate the streaming of protoplasm,an appearance change field was constructed on the basis of the displacement field. Then time series modeling was performed for these frame-level cell dynamic features. Specifically, temporal feature aggregation, and compact encoding in particular, was applied to capturing the video-wide temporal evolution of cell dynamics. A cell-live video dataset was developed to validate the effectiveness of the proposed framework. The experimental results demonstrate that, the proposed method is better than other mainstreaming approaches in measuring and clustering the cell deformation dynamics.