| アイテムタイプ |
共通アイテムタイプ(1) |
| 公開日 |
2025-03-03 |
| タイトル |
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|
タイトル |
Virtual optofluidic time-stretch quantitative phase imaging |
|
言語 |
en |
| 著者 |
Yan, Haochen
Wu, Yunzhao
Zhou, Yuqi
徐, 木貞
Paiè, Petra
Lei, Cheng
Yan, Sheng
Goda, Keisuke
|
| 著作権関連情報 |
|
|
権利情報Resource |
http://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
Copyright (c) 2020 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license(http://creativecommons.org/licenses/by/4.0/). |
|
言語 |
en |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Optofluidic time-stretch quantitative phase imaging (OTS-QPI) is a potent tool for biomedical applications as it enables high-throughput QPI of numerous cells for large-scale single-cell analysis in a label-free manner. However, there are a few critical limitations that hinder OTS-QPI from being widely applied to diverse applications, such as its costly instrumentation and inherent phase-unwrapping errors. Here, to overcome the limitations, we present a QPI-free OTS-QPI method that generates “virtual” phase images from their corresponding bright-field images by using a deep neural network trained with numerous pairs of bright-field and phase images. Specifically, our trained generative adversarial network model generated virtual phase images with high similarity (structural similarity index >0.7) to their corresponding real phase images. This was also supported by our successful classification of various types of leukemia cells and white blood cells via their virtual phase images. The virtual OTS-QPI method is highly reliable and cost-effective and is therefore expected to enhance the applicability of OTS microscopy in diverse research areas, such as cancer biology, precision medicine, and green energy. |
|
言語 |
en |
| 書誌情報 |
en : APL Photonics
巻 5,
号 4,
p. 046103,
発行日 2020-04-13
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| 出版社 |
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|
出版者 |
American Institute of Physics |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1063/1.5134125 |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2378-0967 |
| 研究者情報 |
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|
URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/100001773_ja.html |
| 論文ID（連携） |
|
|
値 |
10449235 |
| 連携ID |
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|
値 |
13090 |
10449235.pdf (7.5 MB)
