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Segmentation of the Zebrafish Brain Vasculature from Light Sheet Fluorescence Microscopy Datasets

View ORCID ProfileElisabeth C. Kugler, Andrik Rampun, View ORCID ProfileTimothy J.A. Chico, Paul A. Armitage
doi: https://doi.org/10.1101/2020.07.21.213843
Elisabeth C. Kugler
1Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Beech Hill Road, Sheffield, S10 2RX United Kingdom
2The Bateson Centre, Firth Court, University of Sheffield, Western Bank, Sheffield, S10 2TN United Kingdom
3Insigneo Institute for in silico Medicine, The Pam Liversidge Building, Sheffield S1 3JD, United Kingdom
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  • ORCID record for Elisabeth C. Kugler
  • For correspondence: ekugler1@sheffield.ac.uk
Andrik Rampun
1Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Beech Hill Road, Sheffield, S10 2RX United Kingdom
3Insigneo Institute for in silico Medicine, The Pam Liversidge Building, Sheffield S1 3JD, United Kingdom
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Timothy J.A. Chico
1Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Beech Hill Road, Sheffield, S10 2RX United Kingdom
2The Bateson Centre, Firth Court, University of Sheffield, Western Bank, Sheffield, S10 2TN United Kingdom
3Insigneo Institute for in silico Medicine, The Pam Liversidge Building, Sheffield S1 3JD, United Kingdom
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Paul A. Armitage
1Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Beech Hill Road, Sheffield, S10 2RX United Kingdom
3Insigneo Institute for in silico Medicine, The Pam Liversidge Building, Sheffield S1 3JD, United Kingdom
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Abstract

Light sheet fluorescent microscopy allows imaging of zebrafish vascular development in great detail. However, interpretation of data often relies on visual assessment and approaches to validate image analysis steps are broadly lacking. Here, we compare different enhancement and segmentation approaches to extract the zebrafish cerebral vasculature, provide comprehensive validation, study segmentation robustness, examine sensitivity, apply the validated method to quantify embryonic cerebrovascular volume, and examine applicability to different transgenic reporter lines. The best performing segmentation method was used to train different deep learning networks for segmentation. We found that U-Net based architectures outperform SegNet. While there was a slight overestimation of vascular volume using the U-Net methodologies, variances were low, suggesting that sensitivity to biological changes would still be obtained.

Highlights

  • General filtering is less applicable than Sato enhancement to enhance zebrafish cerebral vessels.

  • Biological data sets help to overcome the lack of segmentation gold-standards and phantom models.

  • Sato enhancement followed by Otsu thresholding is highly accurate, robust, and sensitive.

  • Direct generalization of the segmentation approach to transgenics, other than the one optimized for, should be treated with caution.

  • Deep learning based segmentation is applicable to the zebrafish cerebral vasculature, with U-Net based architectures outperforming SegNet architectures.

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Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵# Joint Senior Authors

  • Data availability statement. Data are available upon request.

  • Conflict of interest statement. Authors have declared that no conflict of interest exists.

  • Funding bodies. This work was supported by a University of Sheffield, Department of Infection, Immunity and Cardiovascular Disease, Imaging and Modelling Node Studentship and a Bridging Fund from the Insigneo Institute for in silico Medicine, University of Sheffield awarded to EK. The Zeiss Z1 light-sheet microscope was funded via British Heart Foundation Infrastructure Award awarded to TC.

    Funding bodies had no involvement in the study design, or collection, analysis, and interpretation of data.

  • Abbreviations

    BA
    basilar artery
    CNR
    contrast-to-noise ratio
    CtA
    central artery
    dpf
    days post fertilisation
    FWHM
    Full-Width-Half-Maximum
    GF
    General Filtering
    hpf
    hours post fertilisation
    LSFM
    light sheet fluorescence microscopy
    MMCtA
    middle mesencephalic central artery
    PMBC
    primordial midbrain channel
    ROI
    region of interest
    SE
    Sato Enhancement
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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    Segmentation of the Zebrafish Brain Vasculature from Light Sheet Fluorescence Microscopy Datasets
    Elisabeth C. Kugler, Andrik Rampun, Timothy J.A. Chico, Paul A. Armitage
    bioRxiv 2020.07.21.213843; doi: https://doi.org/10.1101/2020.07.21.213843
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    Segmentation of the Zebrafish Brain Vasculature from Light Sheet Fluorescence Microscopy Datasets
    Elisabeth C. Kugler, Andrik Rampun, Timothy J.A. Chico, Paul A. Armitage
    bioRxiv 2020.07.21.213843; doi: https://doi.org/10.1101/2020.07.21.213843

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