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Validating Segmentation of the Zebrafish Vasculature

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Medical Image Understanding and Analysis (MIUA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1065))

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Abstract

The zebrafish is an established vertebrate model to study development and disease of the cardiovascular system. Using transgenic lines and state-of-the-art microscopy it is possible to visualize the vascular architecture non-invasively, in vivo over several days. Quantification of the 3D vascular architecture would be beneficial to objectively and reliably characterise the vascular anatomy. So far, no method is available to automatically quantify the 3D cardiovascular system of transgenic zebrafish, which would enhance their utility as a pre-clinical model. Vascular segmentation is essential for any subsequent quantification, but due to the lack of a segmentation “gold standard” for the zebrafish vasculature, no in-depth assessment of vascular segmentation methods in zebrafish has been performed. In this study, we examine vascular enhancement using the Sato et al. enhancement filter in the Fiji image analysis framework and optimise the filter scale parameter for typical vessels of interest in the zebrafish cranial vasculature; and present methodological approaches to address the lack of a segmentation gold-standard of the zebrafish vasculature.

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References

  1. Gut, P., Reischauer, S., Stainier, D.Y.R., Arnaout, R.: Little fish, big data: zebrafish as a model for cardiovascular and metabolic disease. Physiol. Rev. 97(3), 889–938 (2017)

    Article  Google Scholar 

  2. Kimmel, C.B., Ballard, W.W., Kimmel, S.R., Ullmann, B., Schilling, T.F.: Stages of embryonic development of the zebrafish. Dev. Dyn. 203(3), 253–310 (1995)

    Article  Google Scholar 

  3. Huisken, J., Swoger, J., Del Bene, F., Wittbrodt, J., Stelzer, E.H.K.: Optical sectioning deep inside live embryos by selective plane illumination microscopy. Science 305(5686), 1007–1009 (2004). (New York)

    Article  Google Scholar 

  4. Feng, J., Ip, H.H.S., Cheng, S.H., Chan, P.K.: A relational-tubular (ReTu) deformable model for vasculature quantification of zebrafish embryo from microangiography image series. Comput. Med. Imaging Graph. Off. J. Comput. Med. Imaging Soc. 28(6), 333–344 (2004)

    Article  Google Scholar 

  5. Feng, J., Cheng, S.H., Chan, P.K., Ip, H.H.S.: Reconstruction and representation of caudal vasculature of zebrafish embryo from confocal scanning laser fluorescence microscopic images. Comput. Biol. Med. 35(10), 915–931 (2005)

    Article  Google Scholar 

  6. Feng, J., Ip, H.H.S.: A statistical assembled deformable model (SAMTUS) for vasculature reconstruction. Comput. Biol. Med. 39(6), 489–500 (2009)

    Article  Google Scholar 

  7. Ip, H., Feng, J., Cheng, H.: Automatic segmentation and tracking of vasculature from confocal scanning laser fluorescence microscope images sequences using multi-orientation dissection sections. In: Proceedings of IEEE, pp. 249–252 (2002)

    Google Scholar 

  8. Schneider, S.: Segmentation of zebrafish vasculature. Technical report, MOSAIC group (2015)

    Google Scholar 

  9. Sato, Y., et al.: 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. In: Troccaz, J., Grimson, E., Mösges, R. (eds.) CVRMed/MRCAS -1997. LNCS, vol. 1205, pp. 213–222. Springer, Heidelberg (1997). https://doi.org/10.1007/BFb0029240

    Chapter  Google Scholar 

  10. Tam, S., et al.: Death receptors DR6 and TROY regulate brain vascular development. Dev. Cell 22(2), 403–417 (2012)

    Article  Google Scholar 

  11. Chen, Q., et al.: Haemodynamics-driven developmental pruning of brain vasculature in zebrafish. PLoS Biol. 10(8), 1–18 (2012)

    Article  Google Scholar 

  12. Kugler, E., Chico, T., Armitage, P.: Image analysis in light sheet fluorescence microscopy images of transgenic zebrafish vascular development. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds.) MIUA 2018. CCIS, vol. 894, pp. 343–353. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95921-4_32

    Chapter  Google Scholar 

  13. Kugler, E., Plant, K., Chico, T., Armitage, P.: Enhancement and segmentation workflow for the developing zebrafish vasculature. J. Imaging 5(1), 14 (2019)

    Article  Google Scholar 

  14. Schindelin, J., et al.: Fiji - an open source platform for biological image analysis. Nat. Methods 9(7), 676–682 (2012)

    Article  Google Scholar 

  15. Lawson, N.D., Weinstein, B.M.: In vivo imaging of embryonic vascular development using transgenic zebrafish. Dev. Biol. 248(2), 307–318 (2002)

    Article  Google Scholar 

  16. Chi, N.C., et al.: Foxn4 directly regulates tbx2b expression and atrioventricular canal formation. Genes Dev. 22(6), 734–739 (2008)

    Article  Google Scholar 

  17. Westerfield, M.: The Zebrafish Book: A Guide for Laboratory Use of Zebrafish (Brachydanio Rerio), 2nd edn. University of Oregon Press, Corvallis (1993)

    Google Scholar 

  18. Lim, J.: Two-Dimensional Signal and Image Processing, pp. 469–476. Prentice Hall, Englewood Cliffs (1990)

    Google Scholar 

  19. Sternberg, S.: Biomedical image processing. Computer 16, 22–34 (1983)

    Article  Google Scholar 

  20. Otsu, N.: A threshold selection method from gray-level histograms. Trans. Sys. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  21. D’Agostino, R.B., Belanger, A.: A suggestion for using powerful and informative tests of normality. Am. Stat. 44(4), 316–321 (1990)

    Google Scholar 

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Acknowledgments

This work was supported by a University of Sheffield, Department of Infection, Immunity and Cardiovascular Disease, Imaging and Modelling Node Studentship awarded to EK. The Zeiss Z1 light-sheet microscope was funded via British Heart Foundation Infrastructure Award awarded to TC.

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Correspondence to Elisabeth Kugler .

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Kugler, E., Chico, T., Armitage, P.A. (2020). Validating Segmentation of the Zebrafish Vasculature. In: Zheng, Y., Williams, B., Chen, K. (eds) Medical Image Understanding and Analysis. MIUA 2019. Communications in Computer and Information Science, vol 1065. Springer, Cham. https://doi.org/10.1007/978-3-030-39343-4_23

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  • DOI: https://doi.org/10.1007/978-3-030-39343-4_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39342-7

  • Online ISBN: 978-3-030-39343-4

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