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Stain-free detection of embryo polarization using deep learning.

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Shen, Cheng 
Lamba, Adiyant 
Zhu, Meng 
Zhang, Ray 
Zernicka-Goetz, Magdalena 


Polarization of the mammalian embryo at the right developmental time is critical for its development to term and would be valuable in assessing the potential of human embryos. However, tracking polarization requires invasive fluorescence staining, impermissible in the in vitro fertilization clinic. Here, we report the use of artificial intelligence to detect polarization from unstained time-lapse movies of mouse embryos. We assembled a dataset of bright-field movie frames from 8-cell-stage embryos, side-by-side with corresponding images of fluorescent markers of cell polarization. We then used an ensemble learning model to detect whether any bright-field frame showed an embryo before or after onset of polarization. Our resulting model has an accuracy of 85% for detecting polarization, significantly outperforming human volunteers trained on the same data (61% accuracy). We discovered that our self-learning model focuses upon the angle between cells as one known cue for compaction, which precedes polarization, but it outperforms the use of this cue alone. By compressing three-dimensional time-lapsed image data into two-dimensions, we are able to reduce data to an easily manageable size for deep learning processing. In conclusion, we describe a method for detecting a key developmental feature of embryo development that avoids clinically impermissible fluorescence staining.


Funder: Medical Research Council; doi:

Funder: Cambridge Vice Chancellor’s Award Fund

Funder: Open Philanthropy/Silicon Valley

Funder: Weston Havens Foundations


Animals, Cell Polarity, Coloring Agents, Deep Learning, Embryo, Mammalian, Embryonic Development, Fertilization in Vitro, Humans, Mice, Staining and Labeling

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Springer Science and Business Media LLC
Wellcome Trust (098287/Z/12/Z)