Deep learning-based cytoskeleton segmentation for accurate high-throughput measurement of cytoskeleton density.
Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection...
| Publicado en: | Protoplasma Vol. 262; no. 3; pp. 739 - 752 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184705671&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184705671 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0033183X 2CF jtl: Protoplasma issn: 0033183X maglogo: N pubinfo: dt: May2025 vid: 262 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184705671 181723401 10.1007/s00709-024-02019-9 184705671 ppf: 739 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning-based cytoskeleton segmentation for accurate high-throughput measurement of cytoskeleton density. aug: au: Horiuchi, Ryota Kamimura, Asuka Hanaki, Yuga Matsumoto, Hikari Ueda, Minako Higaki, Takumi affil: https://ror.org/02cgss904 Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-Ku, 860-8555, Kumamoto, Japan sug: ab: Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection. However, the transition to quantitative digital microscopy has introduced new technical challenges, with segmentation of cytoskeleton structures proving particularly demanding. In this study, we examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images of the cortical microtubules in tobacco BY-2 cells. The results showed that, although conventional methods sufficed for measurement of cytoskeleton angles and parallelness, the deep learning-based method significantly improved the accuracy of density measurements. To assess the versatility of the method, we extended our analysis to physiologically significant models in the context of changes in cytoskeleton density, namely Arabidopsis thaliana guard cells and zygotes. The deep learning-based method successfully improved the accuracy of cytoskeleton density measurements for quantitative evaluations of physiological changes in both stomatal movement in guard cells and intracellular polarization in elongating zygotes, confirming its utility in these applications. The results demonstrate the effectiveness of deep learning-based segmentation in providing precise and high-throughput measurements of cytoskeleton density, and has the potential to automate and expedite analyses of large-scale image datasets. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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