Repository logo
 

Stain-free detection of embryo polarization using deep learning.

Published version
Peer-reviewed

Change log

Authors

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

Abstract

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.

Description

Funder: Medical Research Council; doi: http://dx.doi.org/10.13039/501100000265


Funder: Cambridge Vice Chancellor’s Award Fund


Funder: Open Philanthropy/Silicon Valley


Funder: Weston Havens Foundations

Keywords

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

Journal Title

Sci Rep

Conference Name

Journal ISSN

2045-2322
2045-2322

Volume Title

12

Publisher

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