Abstract
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.
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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)
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)
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)
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)
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)
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)
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)
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)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
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Wang, Y., Moussavi, F., Lorenzen, P. (2013). Automated Embryo Stage Classification in Time-Lapse Microscopy Video of Early Human Embryo Development. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40763-5_57
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DOI: https://doi.org/10.1007/978-3-642-40763-5_57
Publisher Name: Springer, Berlin, Heidelberg
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