Quantitative analysis of trabecular bone tissue cryosections via a fully automated neural network-based approach
Cryosectioning is known as a common and well-established histological method, due to its easy accessibility, speed, and cost efficiency. However, the creation of bone cryosections is especially difficult. In this study, a cryosectioning protocol for trabecular bone that offers a relatively cheap and...
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| Hauptverfasser: | , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
April 16, 2024
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| In: |
PLOS ONE
Year: 2024, Jahrgang: 19, Heft: 4, Pages: 1-15 |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0298830 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1371/journal.pone.0298830 Verlag, kostenfrei, Volltext: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0298830 
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| Verfasserangaben: | Christopher Pohl, Moritz Kunzmann, Nico Brandt, Charlotte Koppe, Janine Waletzko-Hellwig, Rainer Bader, Friederike Kalle, Stephan Kersting, Daniel Behrendt, Michael Schlosser, Andreas Hoene |
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| 245 | 1 | 0 | |a Quantitative analysis of trabecular bone tissue cryosections via a fully automated neural network-based approach |c Christopher Pohl, Moritz Kunzmann, Nico Brandt, Charlotte Koppe, Janine Waletzko-Hellwig, Rainer Bader, Friederike Kalle, Stephan Kersting, Daniel Behrendt, Michael Schlosser, Andreas Hoene |
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| 520 | |a Cryosectioning is known as a common and well-established histological method, due to its easy accessibility, speed, and cost efficiency. However, the creation of bone cryosections is especially difficult. In this study, a cryosectioning protocol for trabecular bone that offers a relatively cheap and undemanding alternative to paraffin or resin embedded sectioning was developed. Sections are stainable with common histological dying methods while maintaining sufficient quality to answer a variety of scientific questions. Furthermore, this study introduces an automated protocol for analysing such sections, enabling users to rapidly access a wide range of different stainings. Therefore, an automated ‘QuPath’ neural network-based image analysis protocol for histochemical analysis of trabecular bone samples was established, and compared to other automated approaches as well as manual analysis regarding scattering, quality, and reliability. This highly automated protocol can handle enormous amounts of image data with no significant differences in its results when compared with a manual method. Even though this method was applied specifically for bone tissue, it works for a wide variety of different tissues and scientific questions. | ||
| 650 | 4 | |a Bone imaging | |
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| 650 | 4 | |a DAPI staining | |
| 650 | 4 | |a Histology | |
| 650 | 4 | |a Image analysis | |
| 650 | 4 | |a Imaging techniques | |
| 650 | 4 | |a Neural networks | |
| 650 | 4 | |a Specimen sectioning | |
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