Predicting the future direction of cell movement with convolutional neural networks.
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 moveme...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0221245 |
| _version_ | 1819114716257058816 |
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| author | Shori Nishimoto Yuta Tokuoka Takahiro G Yamada Noriko F Hiroi Akira Funahashi |
| author_facet | Shori Nishimoto Yuta Tokuoka Takahiro G Yamada Noriko F Hiroi Akira Funahashi |
| author_sort | Shori Nishimoto |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T04:49:43Z |
| format | Article |
| id | doaj.art-e79f549b14da4adeb5a243944cc161c6 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T04:49:43Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-e79f549b14da4adeb5a243944cc161c62022-12-21T18:38:31ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01149e022124510.1371/journal.pone.0221245Predicting the future direction of cell movement with convolutional neural networks.Shori NishimotoYuta TokuokaTakahiro G YamadaNoriko F HiroiAkira FunahashiImage-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.https://doi.org/10.1371/journal.pone.0221245 |
| spellingShingle | Shori Nishimoto Yuta Tokuoka Takahiro G Yamada Noriko F Hiroi Akira Funahashi Predicting the future direction of cell movement with convolutional neural networks. PLoS ONE |
| title | Predicting the future direction of cell movement with convolutional neural networks. |
| title_full | Predicting the future direction of cell movement with convolutional neural networks. |
| title_fullStr | Predicting the future direction of cell movement with convolutional neural networks. |
| title_full_unstemmed | Predicting the future direction of cell movement with convolutional neural networks. |
| title_short | Predicting the future direction of cell movement with convolutional neural networks. |
| title_sort | predicting the future direction of cell movement with convolutional neural networks |
| url | https://doi.org/10.1371/journal.pone.0221245 |
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