Skip to main content

Advertisement

Log in

3D level set method for blastomere segmentation of preimplantation embryos in fluorescence microscopy images

Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Detailed and accurate characteristics of preimplantation embryos are fundamental for a deep understanding of their development. Recent studies indicate that various geometric features of cells, such as size, shape, volume, and position play a significant role in embryo growth. However, a quantitative assessment of these characteristics first needs a segmentation of the individual cells. The manual separation and labeling of cells is extremely inefficient, and an automated approach is highly desirable. This paper presents an automatic method for early stage embryo segmentation into its constituent cells and membranes using three-dimensional (3D) data. The input data consist of two Z-stacks of fluorescence microscope images containing nuclei and membranes. The method uses a 3D level set segmentation algorithm. Its evaluation is based on a dataset composed of 20 mouse embryos, each with 4–32 blastomeres. Segmentation accuracy was evaluated by calculating F-scores with ground truth obtained by manually labeling desired regions. We also compared output of our method with the one acquired with a watershed algorithm. The proposed approach was able to achieve more than \(90\%\) accuracy for embryos with 4 and 8 cells, while for embryos with higher number of cells it was lower, reaching \(75\%\) for 32-cell embryo.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Notes

  1. www.github.com/Akazora/ES-3DL.

References

  1. Adalsteinsson, D., Sethian, J.A.: A fast level set method for propagating interfaces. J. Comput. Phys. 118, 269–277 (1995). doi:10.1006/jcph.1995.1098

    Article  MathSciNet  MATH  Google Scholar 

  2. Al-Kofahi, Y., Lassoued, W., Lee, W., Roysam, B.: Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans. Biomed. Eng. 57(4), 841–52 (2010). doi:10.1109/TBME.2009.2035102

    Article  Google Scholar 

  3. Altman, D.G.: Practical Statistics for Medical Research. Chapman Hall, London (1991)

    Google Scholar 

  4. Bedzhov, I., Graham, S.J.L., Leung, C.Y., Zernicka-Goetz, M.: Developmental plasticity, cell fate specification and morphogenesis in the early mouse embryo. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369(1657) (2014). doi:10.1098/rstb.2013.0538

  5. Brox, T., Weickert, J.: Level set segmentation with multiple regions. IEEE Trans. Image Process. 15(10), 3213–3218 (2006). doi:10.1109/TIP.2006.877481

    Article  Google Scholar 

  6. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997). doi:10.1023/A:1007979827043

    Article  MATH  Google Scholar 

  7. Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int. J. Comput. Vis. 72(2), 195–215 (2007). doi:10.1007/s11263-006-8711-1

    Article  Google Scholar 

  8. Dard, N., Louvet-Valle, S., Maro, B.: Orientation of mitotic spindles during the 8- to 16-cell stage transition in mouse embryos. PLoS ONE 4(12), 1–8 (2009). doi:10.1371/journal.pone.0008171

    Article  Google Scholar 

  9. Dufour, A., Shinin, V., Tajbakhsh, S., Guillen-Aghion, N., Olivo-Marin, J.C.: Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces. IEEE Signal Process. Soc. 14, 1396–1410 (2005). doi:10.1109/TIP.2005.852790

    Google Scholar 

  10. Dzyubachyuk, O., van Cappellen, W.A., Essers, J., Nissen, W.J.: Advanced level-set-based cell tracking in time-lapse fluorescence microscopy. IEEE Trans. Med. Imaging 29, 852–867 (2010). doi:10.1109/TMI.2009.2038693

    Article  Google Scholar 

  11. Fernandez, R., Das, P., Mirabet, V., Moscardi, E., Traas, J., Verdeil, J.L., Malandain, G., Godin, C.: Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution. Nat. Methods 7(7), 547–553 (2001). doi:10.1038/nmeth.1472

    Article  Google Scholar 

  12. Gharipour, A., Wee-Chung Liew, A.: Segmentation of cell nuclei in fluorescence microscopy images: an integrated framework using level set segmentation and touching-cell splitting. Pattern Recognit. 58, 1–11 (2016). doi:10.1016/j.patcog.2016.03.030

    Article  Google Scholar 

  13. Giusti, A., Corani, G., Gambardella, L., Magli, C., Gianaroli, L.: Blastomere segmentation and 3D morphology measurements of early embryos from Hoffman modulation contrast image stacks. In: Proceedings of the IEEE International Symposium on Biomedical Imaging, Rotterdam, pp. 13–17 (2010). doi:10.1109/ISBI.2010.5490225

  14. Goldenberg, R., Kimmel, R., Rivlin, E., Rudzsky, M.: Fast geodesic active contours. IEEE Trans. Med. Imaging 10(10), 1467–1475 (2001). doi:10.1109/83.951533

    MathSciNet  Google Scholar 

  15. Hillman, N., Sherman, M.I., Graham, C.: The effect of spatial arrangement on cell determination during mouse development. J. Embryol. Exp. Morphol. 28, 263–278 (1972)

    Google Scholar 

  16. Huang, X., Metaxas, D., Chen, T.: Metamorphs: Deformable shape and texture models. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 496–503 (2004). doi:10.1109/CVPR.2004.1315072

  17. Kong, J., Wang, F., Teodoro, G., Liang, Y., Zhu, Y., Tucker-Burden, C., Brat, D.J.: Automated cell segmentation with 3D fluorescence microscopy images. In: Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 1212–1215 (2015). doi:10.1109/ISBI.2015.7164091

  18. Li, G., Liu, T., Tarokh, A., Nie, J., Guo, L., Mara, A., Holley, S., Wong, S.T.: 3D cell nuclei segmentation based on gradient flow tracking. BMC Cell Biol. 8, 40 (2007). doi:10.1186/1471-2121-8-40

    Article  Google Scholar 

  19. Lou, X., Kang, M., Muoz-Descalzo, S., Hadjantonakis, A.K.: A rapid and efficient 2D/3D nuclear segmentation method for analysis of early mouse embryo and stem cell image data. Stem Cell Rep. 11, 382–397 (2014). doi:10.1016/j.stemcr.2014.01.010

    Article  Google Scholar 

  20. Mandar, D.M., Bosiljka, T., Kazunari, M., Ling, L., Liqun, L.: A global double-fluorescent cre reporter mouse. Genesis 45, 593–605 (2007). doi:10.1002/dvg.20335

    Article  Google Scholar 

  21. Mansouri, A.R., Mitiche, A., Vazquez, C.: Multiregion competition: a level set extension of region competition to multiple region image partitioning. Comput. Vis. Image Underst. 101(3), 137–150 (2006). doi:10.1016/j.cviu.2005.07.008

    Article  Google Scholar 

  22. Meyer, F.: Topographic distance and watershed lines. Signal Process. 38(1), 113–125 (1994). doi:10.1016/0165-1684(94)90060-4

    Article  MATH  Google Scholar 

  23. Nandy, K., Chellappa, R., Kumar, A.A.: Size-invariant cell nucleus segmentation in 3-D microscopy. In: 2012 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp. 37–40 (2012). doi:10.1109/SSIAI.2012.6202447

  24. Nandy, K., Chellappa, R., Kumar, A.: Segmentation of nuclei from 3D microscopy images of tissue via graphcut optimization. IEEE J. Sel. Top. Signal Process. 10(1), 140–150 (2016). doi:10.1109/JSTSP.2015.2505148

    Article  Google Scholar 

  25. Niakan, K.K., Han, J., Pedersen, R.A., Simon, C., Pera, R.A.: Human pre-implantation embryo development. Development 139, 829–841 (2012). doi:10.1242/dev.060426

    Article  Google Scholar 

  26. Nowotschin, S., Hadjantonakis, A.K.: Live imaging mouse embryonic development: seeing is believing and revealing. Methods Mol. Biol. 1092, 405–420 (2014). doi:10.1007/978-1-60327-292-6_24

    Article  Google Scholar 

  27. Ohnishi, Y., Huber, W., Tsumura, A., Kang, M., Xenopoulos, P., Kurimoto, K., Oles, A.K., Arazo-Bravo, M.J., Saitou, M., Hadjantonakis, A.K., Hiiragi, T.: Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages. Nat. Cell Biol. 16, 27–37 (2014). doi:10.1038/ncb2881

    Article  Google Scholar 

  28. Olivier, N., Luengo-Oroz, M.A., Duloquin, L., Faure, E., Savy, T., Veilleux, I., Solinas, X., Débarre, D., Bourgine, P., Santos, A., Peyriéras, N., Beaurepaire, E.: Cell lineage reconstruction of early zebrafish embryos using label-free nonlinear microscopy. Science 329(5994), 967–971 (2010). doi:10.1126/science.1189428

    Article  Google Scholar 

  29. Ortiz De Solorzano, C., Malladi, R., Lelivre, S.A., Lockett, S.J.: Segmentation of nuclei and cells using membrane related protein markers. J. Microsc. 201(3), 404–415 (2001). doi:10.1046/j.1365-2818.2001.00854.x

    Article  MathSciNet  Google Scholar 

  30. Osher, S., Sethian, J.A.: Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations. J. Comput. Phys. 79, 12–49 (1988). doi:10.1016/0021-9991(88)90002-2

    Article  MathSciNet  MATH  Google Scholar 

  31. Pedersen, U.D., Fogh, O.O., Holm, N.O.: A multiphase variational level set approach for modelling human embryos. In: Proceedings of the Workshop on Variational, Geometric and Level Set Methods in Computer Vision, pp. 25–32 (2003)

  32. Rijsbergen, C.J.V.: Information Retrieval, 2nd edn. Butterworth, London (1979)

    MATH  Google Scholar 

  33. Rossant, J., Tam, P.P.: Emerging asymmetry and embryonic patterning in early mouse development. Dev. Cell 7, 155–164 (2004). doi:10.1016/j.devcel.2004.07.012

    Article  Google Scholar 

  34. Sethian, J.A.: A fast marching level set method for monotonically advancing fronts. Proc. Natl. Acad. Sci 93(4), 1591–1595 (1996). doi:10.1073/pnas.93.4.1591

    Article  MathSciNet  MATH  Google Scholar 

  35. Shi, Y., Clem, W.K.: A fast implementation of the level set method without solving partial differential equations. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 97–100 (2005)

  36. Singh, A., Buonassisi, J., Saeedi, P., Havelock, J.: Automatic blastomere detection in day 1 to day 2 human embryo images using partitioned graphs and ellipsoids. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), Paris, pp. 917–921 (2014). doi:10.1109/ICIP.2014.7025184

  37. Stephenson, R.O., Yamanaka, Y., Rossant, J.: Disorganized epithelial polarity and excess trophectoderm cell fate in preimplantation embryos lacking E-cadherin. Development 137(20), 3383–3391 (2010). doi:10.1242/dev.050195

    Article  Google Scholar 

  38. Strnad, P., Gunther, S., Reichmann, J., Krzic, U., Balazs, B., de Medeiros, G., Norlin, N., Hiiragi, T., Hufnagel, L., Ellenberg, J.: Inverted light-sheet microscope for imaging mouse pre-implantation development. Nat. Methods 13(2), 139–142 (2016). doi:10.1038/nmeth.3690

    Article  Google Scholar 

  39. Tarkowski, A.K., Wróblewska, J.: Development of blastomeres of mouse eggs isolated at the 4- and 8-cell stage. Development 18(1), 155–180 (1967)

    Google Scholar 

  40. Wahlby, C., Sintorn, I.M., Erlandsson, F., Borgefors, G., Bengtsson, E.: Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. J. Microsc. 215(1), 67–76 (2004). doi:10.1111/j.0022-2720.2004.01338.x

    Article  MathSciNet  Google Scholar 

  41. Wennekamp, S., Mesecke, S., Nedelec, F., Hiiragi, T.: A self-organization framework for symmetry breaking in the mammalian embryo. Nat. Rev. Mol. Cell Biol. 14, 452–459 (2013). doi:10.1038/nrm3602

  42. Zernicka-Goetz, M., Morris, S.A., Bruce, A.W.: Making a firm decision: multifaceted regulation of cell fate in the early mouse embryo. Nat. Rev. Genet. 10, 467–477 (2009). doi:10.1038/nrg2564

    Article  Google Scholar 

  43. Zhu, S.C., Yuille, A.: Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. In: Proceedings of the Fifth International Conference on Computer Vision, 1995, Cambridge, MA, pp. 416–423 (1995). doi:10.1109/ICCV.1995.466909

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrey Grushnikov.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (mp4 19625 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Grushnikov, A., Niwayama, R., Kanade, T. et al. 3D level set method for blastomere segmentation of preimplantation embryos in fluorescence microscopy images. Machine Vision and Applications 29, 125–134 (2018). https://doi.org/10.1007/s00138-017-0880-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-017-0880-0

Keywords

Navigation