Predicting the future direction of cell movement with convolutional neural networks.

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ID: 37479
2019
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Abstract
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.
Reference Key
nishimoto2019predictingplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nishimoto, Shori;Tokuoka, Yuta;Yamada, Takahiro G;Hiroi, Noriko F;Funahashi, Akira;
Journal PloS one
Year 2019
DOI
10.1371/journal.pone.0221245
URL
Keywords Keywords not found

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