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OrganoidTracker: Efficient cell tracking using machine learning and manual error correction
https://oist.repo.nii.ac.jp/records/1943
https://oist.repo.nii.ac.jp/records/194308f6ccec-d325-4648-8985-208ec2973b0f
名前 / ファイル | ライセンス | アクション |
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Kok-2020-OrganoidTracker_ Efficient cell track (2.1 MB)
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Creative Commons Attribution 4.0 International(https://creativecommons.org/licenses/by/4.0/)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2021-02-24 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | OrganoidTracker: Efficient cell tracking using machine learning and manual error correction | |||||
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 | |||||
著者(英) |
Kok, Rutger N. U.
× Kok, Rutger N. U.× Hebert, Laetitia× Huelsz-Prince, Guizela× Goos, Yvonne J.× Zheng, Xuan× Bozek, Katarzyna× Stephens, Greg J.× Tans, Sander J.× van Zon, Jeroen S. |
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書誌情報 |
en : PLOS ONE 巻 15, 号 10, p. e0240802, 発行日 2020-10-22 |
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抄録 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Time-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and homeostasis at the single-cell level. As tracking these cells by hand is prohibitively time consuming, automation using a computer program is required. Unfortunately, organoids have a high cell density and fast cell movement, which makes automated cell tracking difficult. In this work, a semi-automated cell tracker has been developed. To detect the nuclei, we use a machine learning approach based on a convolutional neural network. To form cell trajectories, we link detections at different time points together using a min-cost flow solver. The tracker raises warnings for situations with likely errors. Rapid changes in nucleus volume and position are reported for manual review, as well as cases where nuclei divide, appear and disappear. When the warning system is adjusted such that virtually error-free lineage trees can be obtained, still less than 2% of all detected nuclei positions are marked for manual analysis. This provides an enormous speed boost over manual cell tracking, while still providing tracking data of the same quality as manual tracking. | |||||
出版者 | ||||||
出版者 | PLOS | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1932-6203 | |||||
PubMed番号 | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | PMID | |||||
関連識別子 | info:pmid/33091031 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | info:doi/10.1371/journal.pone.0240802 | |||||
権利 | ||||||
権利情報 | © 2020 Kok et al. | |||||
関連サイト | ||||||
識別子タイプ | URI | |||||
関連識別子 | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240802 | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 |