Skip to content
BY 4.0 license Open Access Published by De Gruyter Open Access July 27, 2020

Automated segmentation of thick confocal microscopy 3D images for the measurement of white matter volumes in zebrafish brains

  • Sylvain Lempereur EMAIL logo , Arnim Jenett , Elodie Machado , Ignacio Arganda-Carreras , Matthieu Simion , Pierre Affaticati , Jean-Stéphane Joly and Hugues Talbot


Tissue clearing methods have boosted the microscopic observations of thick samples such as whole-mount mouse or zebrafish. Even with the best tissue clearing methods, specimens are not completely transparent and light attenuation increases with depth, reducing signal output and signal-to-noise ratio. In addition, since tissue clearing and microscopic acquisition techniques have become faster, automated image analysis is now an issue. In this context, mounting specimens at large scale often leads to imperfectly aligned or oriented samples, which makes relying on predefined, sample-independent parameters to correct signal attenuation impossible.

Here, we propose a sample-dependent method for contrast correction. It relies on segmenting the sample, and estimating sample depth isosurfaces that serve as reference for the correction. We segment the brain white matter of zebrafish larvae. We show that this correction allows a better stitching of opposite sides of each larva, in order to image the entire larva with a high signal-to-noise ratio throughout. We also show that our proposed contrast correction method makes it possible to better recognize the deep structures of the brain by comparing manual vs. automated segmentations. This is expected to improve image observations and analyses in high-content methods where signal loss in the samples is significant.

MSC 2010: 68U10; 92C55


[1] Pierre Affaticati, Matthieu Simion, Elodie De Job, Laurie Rivière, Jean-Michel Hermel, Elodie Machado, Jean-Stéphane Joly, and Arnim Jenett. zPACT: Tissue Clearing and Immunohistochemistry on Juvenile Zebrafish Brain. Bio-Protocol, 7(23), 2017.10.21769/BioProtoc.2636Search in Google Scholar PubMed PubMed Central

[2] Amin Allalou, Yuelong Wu, Mostafa Ghannad-Rezaie, Peter M. Eimon, and Mehmet Fatih Yanik. Automated deep-phenotyping of the vertebrate brain. eLife, 6:1–26, 2017.10.7554/eLife.23379Search in Google Scholar PubMed PubMed Central

[3] François Brion, Yann Le Page, Benjamin Piccini, Olivier Cardoso, Sok Keng Tong, Bon chu Chung, and Olivier Kah. Screening estrogenic activities of chemicals or mixtures in vivo using transgenic (cyp19a1b-GFP) zebrafish embryos. PLoS ONE, 7(5):e36069, may 2012.10.1371/journal.pone.0036069Search in Google Scholar PubMed PubMed Central

[4] Jonathan Cachat, Adam Stewart, Eli Utterback, Peter Hart, Siddharth Gaikwad, Keith Wong, Evan Kyzar, Nadine Wu, and Allan V. Kalueff. Three-dimensional neurophenotyping of adult zebrafish behavior. PLoS ONE, 6(3), 2011.10.1371/journal.pone.0017597Search in Google Scholar PubMed PubMed Central

[5] Fabio A.M. Cappabianco, Pedro F.O. Ribeiro, Paulo A.V. De Miranda, and Jayaram K. Udupa. A General and Balanced Region-Based Metric for Evaluating Medical Image Segmentation Algorithms. Proceedings - International Conference on Image Processing, ICIP, 2019-Septe:1525–1529, 2019.10.1109/ICIP.2019.8803083Search in Google Scholar

[6] M. R. Cronan, A. F. Rosenberg, S. H. Oehlers, J. W. Saelens, D. M. Sisk, K. L. Jurcic Smith, S. Lee, and D. M. Tobin. CLARITY and PACT-based imaging of adult zebrafish and mouse for whole-animal analysis of infections. Disease Models & Mechanisms, 8(12):1643–1650, 2015.10.1242/dmm.021394Search in Google Scholar PubMed PubMed Central

[7] W Driever, L Solnica-Krezel, A F Schier, S C Neuhauss, J Malicki, D L Stemple, D Y Stainier, F Zwartkruis, S Abdelilah, Z Rangini, J Belak, and C Boggs. A genetic screen for mutations affecting embryogenesis in zebrafish. Development (Cambridge, England), 123(1):37–46, 1996.10.1242/dev.123.1.37Search in Google Scholar PubMed

[8] Jason J Early, Katy LH Cole, Jill M Williamson, Matthew Swire, Hari Kamadurai, Marc Muskavitch, and David A Lyons. An automated high-resolution in vivo screen in zebrafish to identify chemical regulators of myelination. eLife, 7:1–31, 2018.10.7554/eLife.35136Search in Google Scholar PubMed PubMed Central

[9] Andrey Fedorov, Reinhard Beichel, Jayashree Kalphaty-Cramer, Julien Finet, J-C Fillion-Robbin, Sonia Pujol, Christian Bauer, Dominique Jennings, Fiona Fennessy, Milan Sonka, John Buatti, Stephen Aylward, James V. Miller, Steve Pieper, and Ron Kikinis. 3D slicers as an image computing platform for thw quantitative imaging network. Magnetic resonance imaging, 30(9):1323–1341, 2012.10.1016/j.mri.2012.05.001Search in Google Scholar PubMed PubMed Central

[10] Jochen Gehrig, Markus Reischl, Eva Kalmár, Marco Ferg, Yavor Hadzhiev, Andreas Zaucker, Chengyi Song, Simone Schindler, Urban Liebel, and Ferenc Müller. Automated high-throughput mapping of promoter-enhancer interactions in zebrafish embryos. Nature methods, 6(12):911–916, 2009.10.1038/nmeth.1396Search in Google Scholar PubMed

[11] Stefan Klein, Marius Staring, Keelin Murphy, Max A. Viergever, and Josien P W Pluim. Elastix: A toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging, 29(1):196–205, 2010.10.1109/TMI.2009.2035616Search in Google Scholar

[12] Jürgen Mayer, Alexandre Robert-moreno, James Sharpe, and Jim Swoger. Attenuation artifacts in light sheet fluorescence microscopy corrected by OPTiSPIM. Light: Science & Applications, 2018.10.1038/s41377-018-0068-zSearch in Google Scholar PubMed PubMed Central

[13] Merlin Mikulewitsch, Matthias Marcus Auerswald, Axel Von Freyberg, and Andreas Fischer. Geometry Measurement of Submerged Metallic Micro-Parts Using Confocal Fluorescence Microscopy. Nanomanufacturing and Metrology, 1(3):171–179, 2018.10.1007/s41871-018-0019-6Search in Google Scholar

[14] Raghuveer Parthasarathy, Ryan P. Baker, Edouard A. Hay, Michael J. Taormina, Savannah L. Logan, and Christopher Dudley. Automated high-throughput light-sheet fluorescence microscopy of larval zebrafish. Plos One, 13(11):e0198705, 2018.10.1371/journal.pone.0198705Search in Google Scholar PubMed PubMed Central

[15] Olaf Ronneberger, Kun Liu, Meta Rath, Dominik Rue, Thomas Mueller, Henrik Skibbe, Benjamin Drayer, Thorsten Schmidt, Alida Filippi, Roland Nitschke, Thomas Brox, Hans Burkhardt, and Wolfgang Driever. ViBE-Z: A framework for 3D virtual colocalization analysis in zebrafish larval brains. Nature Methods, 9(7):735–742, jun 2012.10.1038/nmeth.2076Search in Google Scholar PubMed

[16] Suraiya Saleem and Rajaretinam Rajesh Kannan. Zebrafish: an emerging real-time model system to study Alzheimer’s disease and neurospecific drug discovery. Cell Death Discovery, 5(1):45, 2018.10.1038/s41420-018-0109-7Search in Google Scholar

[17] Mark Schutera, Thomas Dickmeis, Marina Mione, Ravindra Peravali, Daniel Marcato, Markus Reischl, Ralf Mikut, and Christian Pylatiuk. Automated Phenotype Pattern Recognition of Zebrafish for High - throughput Screening. Bioengineered, 5979(September), 2016.10.1080/21655979.2016.1197710Search in Google Scholar PubMed PubMed Central

[18] Pier-Luc Tardif, Marie-jeanne Bertrand, Maxime Abran, Alexandre Castonguay, Joël Lefebvre, Barbara Stähli, Nolwenn Merlet, Teodora Mihalache-Avram, Pascale Geoffroy, Mélanie Mecteau, David Busseuil, Feng Ni, Abedelnasser Abulrob, Éric Rhéaume, Philippe L’Allier, Jean-claude Tardif, and Frédéric Lesage. Validating Intravascular Imaging with Serial Optical Coherence Tomography and Confocal Fluorescence Microscopy. International Journal of Molecular Sciences, 17(12):2110, dec 2016.10.3390/ijms17122110Search in Google Scholar PubMed PubMed Central

[19] Elisabet Teixidó, Tobias R Kießling, Eckart Krupp, Celia Quevedo, Arantza Muriana, and Stefan Scholz. Automated morphological feature assessment for zebrafish embryo developmental toxicity screens. Toxicological Sciences, pages 1–12, 2018.10.1093/toxsci/kfy250Search in Google Scholar PubMed PubMed Central

[20] Jennifer Brooke Treweek and Viviana Gradinaru. Extracting structural and functional features of widely distributed biological circuits with single cell resolution via tissue clearing and delivery vectors. Current Opinion in Biotechnology, 40:193–207, 2016.10.1016/j.copbio.2016.03.012Search in Google Scholar PubMed PubMed Central

[21] Y Uanhao G Uo, W Outer J V Eneman, H Erman P S Paink, and F O N S J V Erbeek. Three-dimensional reconstruction and measurements of zebrafish larvae from high-throughput axial-view in vivo imaging. Biomedical Optics Express, 8(5):23–32, 2017.10.1364/BOE.8.002611Search in Google Scholar PubMed PubMed Central

[22] Jonas N Wittbrodt, Urban Liebel, and Jochen Gehrig. Generation of orientation tools for automated zebrafish screening assays using desktop 3D printing. BMC biotechnology, 14(1):36, 2014.10.1186/1472-6750-14-36Search in Google Scholar PubMed PubMed Central

[23] Tingting Yu, Yisong Qi, Hui Gong, Qingming Luo, and Dan Zhu. Optical clearing for multiscale biological tissues. Journal of Biophotonics, 11(2), 2018.10.1002/jbio.201700187Search in Google Scholar PubMed

Received: 2019-11-21
Accepted: 2020-04-20
Published Online: 2020-07-27

© 2020 Sylvain Lempereur et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Downloaded on 1.12.2023 from
Scroll to top button