Computational Analysis of Cell Dynamics in Videos with Hierarchical-Pooled Deep-Convolutional Features.

Journal: Journal of computational biology : a journal of computational molecular cell biology
PMID: 29694245

Abstract

Computational analysis of cellular appearance and its dynamics is used to investigate physiological properties of cells in biomedical research. In consideration of the great success of deep learning in video analysis, we first introduce two-stream convolutional networks (ConvNets) to automatically learn the biologically meaningful dynamics from raw live-cell videos. However, the two-stream ConvNets lack the ability to capture long-range video evolution. Therefore, a novel hierarchical pooling strategy is proposed to model the cell dynamics in a whole video, which is composed of trajectory pooling for short-term dynamics and rank pooling for long-range ones. Experimental results demonstrate that the proposed pipeline effectively captures the spatiotemporal dynamics from the raw live-cell videos and outperforms existing methods on our cell video database.

Authors

  • Fengqian Pang
    Department of Information and Electronics, Beijing Institute of Technology , Beijing, China .
  • Heng Li
    Department of Anesthesiology, Affiliated Nanhua Hospital, University of South China, Hengyang 421002, Hunan Province, China.
  • Yonggang Shi
    Department of Information and Electronics, Beijing Institute of Technology , Beijing, China .
  • Zhiwen Liu
    Guangdong Provincial Key Laboratory of Protein Function and Regulation in Agricultural Organisms, College of Life Sciences, South China Agricultural University, Guangzhou, China.

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