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

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.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

Zebrafish vascular quantification: a tool for quantification of three-dimensional zebrafish cerebrovascular architecture by automated image analysis

“…Vascular enhancement was performed assuming local vessel tubularity using the Fiji Sato enhancement filter ‘Analyse>Tubeness’ ( Sato et al, 1998 ) with an optimised parameter set as described previously ( Kugler et al, 2018 , 2019a , b ). Enhanced images were segmented using Otsu thresholding ( Otsu, 1979 ) as described in previously ( Kugler et al, 2018 , 2019a ).…”

“…We therefore previously developed and validated a zebrafish vascular segmentation workflow and examined its sensitivity, robustness, and accuracy 7,14 . We applied this to the transgenic Tg(kdrl:HRAS-mCherry) s916 15 using Sato enhancement and Otsu thresholding 6,16,17 .…”

“…Accurate segmentation is the foundation of subsequent analysis and quantification. We therefore previously developed and validated a zebrafish vascular segmentation workflow and examined its sensitivity, robustness and accuracy ( Kugler et al, 2020a , b preprint). We applied this to the transgenic Tg(kdrl:HRAS-mCherry) s916 ( Chi et al, 2008 ) using Sato enhancement and Otsu thresholding ( Kugler et al, 2019a ; Sato et al, 1998 ; Otsu, 1979 ).…”

3D quantification of zebrafish cerebrovascular architecture by automated image analysis of light sheet fluorescence microscopy datasets

“…Vascular enhancement was performed assuming local vessel tubularity using the Fiji Sato enhancement filter “Analyse>Tubeness” 28 with an optimised parameter set as described previously 10,11,14 . Enhanced images were segmented using Otsu thresholding 29 as described in 10,11 .…”

Deep learning is widely applicable to phenotyping embryonic development and disease

“…However, CNNs have also been applied to more complex tasks, such as reconstruction of the entire mouse brain vasculature ( Kirst et al, 2020 ; Todorov et al, 2020 ), in vivo quantification of cancer metastasis and in toto reconstruction of intact human organs ( Pan et al, 2019 ; Zhao et al, 2020 ). In embryology, deep learning has been used for Drosophila animal pose estimation, to map synaptic brain connections ( Buhmann et al, 2021 ; Graving et al, 2019 ; Günel et al, 2019 ), in C. elegans phenotyping ( Hakim et al, 2018 ; Saberi-Bosari et al, 2020 ) and in analysis of zebrafish beating hearts or vessels ( Akerberg et al, 2019 ; Kugler et al, 2020 preprint; Wen et al, 2021 ; Zhang et al, 2021 ), among other applications. Nevertheless, deep learning remains under-used in developmental biology ( Villoutreix, 2021 ).…”