ArticleJournal of computational biology : a journal of computational molecular cell biology2018
Computational Analysis of Cell Dynamics in Videos with Hierarchical-Pooled Deep-Convolutional Features.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Analyzing temporal dynamics of cell deformation and intracellular movement with video feature aggregation.Biomedical engineering online · 2019Article
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4 authors.
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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.
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