| ID | 65253 |
| FullText URL | |
| Author |
Takao, Daisuke
Department of Cell Biology and Anatomy and International Research Center for Neurointelligence (WPI-IRCN), Graduate School of Medicine, The University of Tokyo
Kyunai, Yuki M.
Faculty of Engineering, Department of Applied Chemistry and Biotechnology, Okayama University
Okada, Yasushi
Department of Cell Biology and Anatomy and International Research Center for Neurointelligence (WPI-IRCN), Graduate School of Medicine, The University of Tokyo
Satoh, Ayano
Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University
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| Abstract | The visual classification of cell images according to differences in the spatial patterns of subcellular structure is an important methodology in cell and developmental biology. Experimental perturbation of cell function can induce changes in the spatial distribution of organelles and their associated markers or labels. Here, we demonstrate how to achieve accurate, unbiased, high-throughput image classification using an artificial intelligence (AI) algorithm. We show that a convolutional neural network (CNN) algorithm can classify distinct patterns of Golgi images after drug or siRNA treatments, and we review our methods from cell preparation to image acquisition and CNN analysis.
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| Keywords | Convolutional neural network
Image classification
Golgins
Golgi
Microtubule
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| Note | This is an Accepted Manuscript of a protocol published by Humana New York.
Molecular Biology, vol 2557.
This fulltext file will be available in Dec. 2024.
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| Published Date | 2022-12-14
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| Publication Title |
Golgi
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| Publisher | Humana New York
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| Start Page | 275
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| End Page | 285
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| Content Type |
Book
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| language |
English
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| OAI-PMH Set |
岡山大学
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| Copyright Holders | © 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
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| File Version | author
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| PubMed ID | |
| DOI | |
| Related Url | isVersionOf https://doi.org/10.1007/978-1-0716-2639-9_18
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| Citation | Takao, D., Kyunai, Y.M., Okada, Y., Satoh, A. (2023). A Primer on Deep Learning-Based Cellular Image Classification of Changes in the Spatial Distribution of the Golgi Apparatus After Experimental Manipulation. In: Wang, Y., Lupashin, V.V., Graham, T.R. (eds) Golgi. Methods in Molecular Biology, vol 2557. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2639-9_18
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| Funder Name |
Japan Society for the Promotion of Science
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| 助成番号 | 18K06233
18K06133
19H05974
19H05975
21K06163
21H04708
21H05028
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