Predicting the future direction of cell movement with convolutional neural networks

Shori Nishimoto, Yuta Tokuoka, Takahiro Yamada, Noriko F. Hiroi, Akira Funahashi

研究成果: Article

1 引用 (Scopus)

抜粋

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.

元の言語English
記事番号e0221245
ジャーナルPloS one
14
発行部数9
DOI
出版物ステータスPublished - 2019 1 1

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

フィンガープリント Predicting the future direction of cell movement with convolutional neural networks' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用