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.
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
