Predicting the future direction of cell movement with convolutional neural networks

PLoS One. 2019 Sep 4;14(9):e0221245. doi: 10.1371/journal.pone.0221245. eCollection 2019.


Image-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell movement. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future direction of cell movement from current cell shape, and can be used to automatically identify those morphological features that influence future cell movement.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Cell Line
  • Cell Movement
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Mice
  • Microscopy
  • NIH 3T3 Cells
  • Neural Networks, Computer*
  • Time-Lapse Imaging

Grants and funding

The research was funded to AF by a JSPS KAKENHI Grant (Number 16H04731), The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.