Automated Embryo Stage Classification in Time-Lapse Microscopy Video of Early Human Embryo Development

  • Yu Wang
  • Farshid Moussavi
  • Peter Lorenzen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)


The accurate and automated measuring of durations of certain human embryo stages is important to assess embryo viability and predict its clinical outcomes in in vitro fertilization (IVF). In this work, we present a multi-level embryo stage classification method to identify the number of cells at every time point of a time-lapse microscopy video of early human embryo development. The proposed method employs a rich set of hand-crafted and automatically learned embryo features for classification and avoids explicit segmentation or tracking of individual embryo cells. It was quantitatively evaluated using a total of 389 human embryo videos, resulting in a 87.92% overall embryo stage classification accuracy.


embryo stage classification adaboost bag of features 


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  1. 1.
    Wong, C., Loewke, K.E., Bossert, N.L., Behr, B., De Jonge, C.J., Baer, T.M., Reijo Pera, R.A.: Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage. Nature Biotechnology 28(10), 1115–1121 (2010)CrossRefGoogle Scholar
  2. 2.
    Yang, F., Mackey, M.A., Ianzini, F., Gallardo, G., Sonka, M.: Cell Segmentation, Tracking, and Mitosis Detection Using Temporal Context. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 302–309. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Huh, S., Ker, D.F., Bise, R., Chen, M., Kanade, T.: Automated mitosis detection of stem cell populations in phase-contrast microscopy images. IEEE Transactions on Medical Imaging 30(3), 586–596 (2011)CrossRefGoogle Scholar
  4. 4.
    El-Labban, A., Zisserman, A., Toyoda, Y., Bird, A.W., Hyman, A.: Discriminative Semi-Markov Models for Automated Mitotic Phase Labelling. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 760–763 (2012)Google Scholar
  5. 5.
    Harder, N., Mora-Bermúdez, F., Godinez, W.J., Ellenberg, J., Eils, R., Rohr, K.: Automated analysis of the mitotic phases of human cells in 3D fluorescence microscopy image sequences. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 840–848. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Li, K., Miller, E.D., Chen, M., Kanade, T., Weiss, L.E., Campbell, P.G.: Computer vision tracking of stemness. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp. 847–850 (2009)Google Scholar
  7. 7.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar
  8. 8.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2169–2178 (2006)Google Scholar
  9. 9.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yu Wang
    • 1
  • Farshid Moussavi
    • 1
  • Peter Lorenzen
    • 1
  1. 1.Auxogyn, Inc.Menlo ParkUSA

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