Image Analysis in Light Sheet Fluorescence Microscopy Images of Transgenic Zebrafish Vascular Development

Kugler, Elisabeth; Chico, Timothy J. A.; Armitage, Paul A.

doi:10.1007/978-3-319-95921-4_32

Cited by 10 publications

(35 citation statements)

References 37 publications

(58 reference statements)

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“…Light-Sheet Microscopy succeeded in imaging the neural activity in two colors at 23 frames per second for over 10 min, which granted an opportunity to record the infrequent seizure crisis in mutant type, concluded that zebrafish might be used to mimic epilepsy in human [35]. Hence zebrafish became a recognized model in studying vascular development and disease in vivo [36].…”

Section: Zebrafish By Light-sheet Microscopymentioning

confidence: 99%

Highlights on selected microscopy techniques to study zebrafish developmental biology

Abu-Siniyeh

Al-Zyoud

2020

Lab Anim Res

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Bio-imaging is a tedious task when it concerns exploring cell functions, developmental mechanisms, and other vital processes in vivo. Single-cell resolution is challenging due to different issues such as sample size, the scattering of intact and opaque tissue, pigmentation in untreated animals, the movement of living organs, and maintaining the sample under physiological conditions. These factors might lead researchers to implement microscopy techniques with a suitable animal model to mimic the nature of the living cells. Zebrafish acquired its prestigious reputation in the biomedical research field due to its transparency under advanced microscopes. Therefore, various microscopy techniques, including Multi-Photon, Light-Sheet Microscopy, and Second Harmonic Generation, simplify the discovery of different types of internal functions in zebrafish. In this review, we briefly discuss three recent microscopy techniques that are being utilized because they are non-invasive in investigating developmental events in zebrafish embryo and larvae.

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Section: Zebrafish By Light-sheet Microscopymentioning

confidence: 99%

Highlights on selected microscopy techniques to study zebrafish developmental biology

Abu-Siniyeh

Al-Zyoud

2020

Lab Anim Res

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“…Previously, Kugler et al presented methods to enhance the cerebral vasculature in LSFM data using general filtering (GF; Median Filter and Rolling ball) (Kugler et al, 2018) and enhancement utilizing the Hessian matrix with the assumption of local vessel tubularity, based on the filter proposed by Sato et al (Kugler et al, 2019;Sato et al, 1997). This was further complemented by investigation of different segmentation approaches, which were readily implemented in the Fiji image analysis framework (Schindelin et al, 2012), but no validation of the suggested approach was performed.…”

Section: Previous Work Aiming To Quantify the Zebrafish Cerebral Vascmentioning

confidence: 99%

“…(i) Dataset with controlled decrease of vascular contrast-to-noise ratio (CNR) by decrease of laser power during repeated acquisition (Kugler et al, 2018) (laser power (LP) 1.2%, 0.8% and 0.4%; exposure 30ms for all; 4dpf; n=10 embryos from 2 experimental repeats). Augmented data were produced from LP 1.2% by addition of Gaussian noise with mean of zero and standard deviation of 50 using Fiji (Schindelin et al, 2012).…”

Section: Transgenic Zebrafishmentioning

confidence: 99%

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

Kugler

Rampun

Chico

et al. 2020

Preprint

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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.HighlightsGeneral 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.Graphical Abstract

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“…An estimate of background noise was obtained by measuring the standard deviation (σ) in a region of interest placed outside of the fish. CNR (Equation (1)) was calculated, as below: Sample motion artefact assessment and correction were conducted as described in [13]. Two approaches for vascular enhancement were evaluated.…”

Section: Contrast-to-noise Ratio (Cnr)mentioning

confidence: 99%

“…Two approaches for vascular enhancement were evaluated. The first utilised an optimised general filtering approach for image artefact and noise reduction [13], while the second was based around using image gradient information for enhancing tubular structures. Both methods were performed on data from the Tg(kdrl:HRAS-mCherry) s916 line after 3D slice-by-slice motion correction as follows:…”

Section: Contrast-to-noise Ratio (Cnr)mentioning

confidence: 99%

Enhancement and Segmentation Workflow for the Developing Zebrafish Vasculature

et al. 2019

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Zebrafish have become an established in vivo vertebrate model to study cardiovascular development and disease. However, most published studies of the zebrafish vascular architecture rely on subjective visual assessment, rather than objective quantification. In this paper, we used state-of-the-art light sheet fluorescence microscopy to visualize the vasculature in transgenic fluorescent reporter zebrafish. Analysis of image quality, vascular enhancement methods, and segmentation approaches were performed in the framework of the open-source software Fiji to allow dissemination and reproducibility. Here, we build on a previously developed image processing pipeline; evaluate its applicability to a wider range of data; apply and evaluate an alternative vascular enhancement method; and, finally, suggest a work-flow for successful segmentation of the embryonic zebrafish vasculature.

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Image Analysis in Light Sheet Fluorescence Microscopy Images of Transgenic Zebrafish Vascular Development

Cited by 10 publications

References 37 publications

Highlights on selected microscopy techniques to study zebrafish developmental biology

Highlights on selected microscopy techniques to study zebrafish developmental biology

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

Enhancement and Segmentation Workflow for the Developing Zebrafish Vasculature

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