Abstract
Recently, it has been shown that early division patterns, such as cell division timing biomarkers, are crucial to predict human embryo viability. Precise and accurate measurement of these markers requires cell lineage analysis to identify normal and abnormal division patterns. However, current approaches to early-stage embryo analysis only focus on estimating the number of cells and their locations, thus failing to detect abnormal division patterns and potentially yielding incorrect timing biomarkers. In this work we propose an automated tool that can perform lineage tree analysis up to the 5-cell stage, which is sufficient to accurately compute all the known important biomarkers. To this end, we introduce a CRF-based cell localization framework. We demonstrate the benefits of our approach on a data set of 22 human embryos, resulting in correct identification of all abnormal division patterns in the data set.
We are grateful to Auxogyn, Inc. for their valuable support of this project.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Amat, F., Lemon, W., Mossing, D.P., McDole, K., Wan, Y., Branson, K., Myers, E.W., Keller, P.J.: Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data. Nature Methods (2014)
Chen, A.A., Tan, L., Suraj, V., Pera, R.R., Shen, S.: Biomarkers identified with TL imaging: discovery, validation, and practical app. Fertility and Sterility (2013)
El-Labban, A., Zisserman, A., Toyoda, Y., Bird, A.W., Hyman, A.: Discriminative semi-markov models for automated mitotic phase labelling. In: ISBI (2012)
Khan, A., Gould, S., Salzmann, M.: Automated monitoring of human embryonic cells up to the 5-cell stage in time-lapse microscopy images. In: ISBI (2015)
Khan, A., Gould, S., Salzmann, M.: A linear chain markov model for detection and localization of cells in early stage embryo development. In: WACV (2015)
Li, K., Miller, E., Chen, M., Kanade, T., Weiss, L., Campbell, P.: Computer vision tracking of stemness. In: ISBI (2008)
Liu, A.-A., Li, K., Kanade, T.: Mitosis sequence detection using hidden conditional random fields. In: ISBI (2010)
Lou, X., Hamprecht, F.: Structured learning for cell tracking. In: NIPS (2011)
Meseguer, M., Herrero, J., Tejera, A., Hilligse, K.M., Ramsing, N.B., Jose, R.: The use of morphokinetics as a predictor of embryo implantation. HR (2011)
Moussavi, F., Yu, W., Lorenzen, P., Oakley, J., Russakoff, D., Gould, S.: A unified graphical models framework for automated mitosis detection in human embryos. IEEE Trans. Med. Imaging 1551–1562 (2014)
Schiegg, M., Hanslovsky, P., Kausler, B.X., Hufnagel, L., Hamprecht, F.A.: Conservation tracking. In: ICCV (2013)
Wang, Y., Moussavi, F., Lorenzen, P.: Automated embryo stage classification in TLM video of early human embryo development. In: MICCAI (2013)
Wong, C., Loewke, K., Bossert, N., Behr, B., Jonge, C.D., Baer, T., Pera, R.R.: Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage. Nature Bio. (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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Khan, A., Gould, S., Salzmann, M. (2015). Detecting Abnormal Cell Division Patterns in Early Stage Human Embryo Development. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds) Machine Learning in Medical Imaging. MLMI 2015. Lecture Notes in Computer Science(), vol 9352. Springer, Cham. https://doi.org/10.1007/978-3-319-24888-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-24888-2_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24887-5
Online ISBN: 978-3-319-24888-2
eBook Packages: Computer ScienceComputer Science (R0)