Automated staging of zebrafish embryos with deep learning

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Abstract

The zebrafish ( Danio rerio ) is an important biomedical model organism used in many disciplines. The phenomenon of developmental delay in zebrafish embryos has been widely reported as part of a mutant or treatment-induced phenotype. However, the detection and quantification of these delays is often achieved through manual observation, which is both time-consuming and subjective. We present KimmelNet, a deep learning model trained to predict embryo age (hours post fertilisation) from 2D brightfield images. KimmelNet’s predictions agree closely with established staging methods and can detect developmental delays between populations with high confidence using as few as 100 images. Moreover, KimmelNet generalises to previously unseen data, with transfer learning enhancing its performance. With the ability to analyse tens of thousands of standard brightfield microscopy images on a timescale of minutes, we envisage that KimmelNet will be a valuable resource for the developmental biology community. Furthermore, the approach we have used could easily be adapted to generate models for other organisms.

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  1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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    Reply to the reviewers

    Reviewer #1 (Evidence, reproducibility and clarity):

    In this manuscript the author is presenting a deep-learning model used to predict the development stage of zebrafish embryo. A robust method that can accurately classify a zebrafish into different development stages is highly relevant for many researchers working with zebrafish and hence the importance in developing methods like this is high.

    The manuscript is overall ok. However, more data is needed to convince the reader that the method is robust enough to work with other samples in other labs. This would greatly improve the impact of the publication.

    We agree with the reviewer and have included in …

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    Referee #3

    Evidence, reproducibility and clarity

    Summary:

    Properly staging embryos of zebrafish embryos is important, yet provides challenging since it can depend on many factors, such as temperature, water quality, fish population density, etc. Here, the authors provide a deep-learning-based model called KimmelNet that allows the prediction of the age of zebrafish embryos, using 2D brightfield images. The technique is robust to weak measurement noise and can also be used to identify developmental delays from a very small number of experimental data.

    The code is accessible to the reader, open-source and should be useable by experimentalists without huge effort.

    Major comments:

    I …

  3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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    Referee #2

    Evidence, reproducibility and clarity

    Summary

    The paper "Automated staging of zebrafish embryos with KimmelNet" by Barry et al., presents a method to automatically stage developmental timepoints of zebrafish embryos based on convolutional neural networks (CNN). The authors show that a CNN trained on ~20k images can determine time post fertilization on test-image sets with an accuracy on the range of a few hours. This technique undoubtedly has the potential to become very useful for any zebrafish researchers interested in developmental timing as it eases analysis and removes potential subjective bias.

    Major comments

    In its current form the paper lacks sufficient graph …

  4. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

    Learn more at Review Commons


    Referee #1

    Evidence, reproducibility and clarity

    In this manuscript the author is presenting a deep-learning model used to predict the development stage of zebrafish embryo. A robust method that can accurately classify a zebrafish into different development stages is highly relevant for many researchers working with zebrafish and hence the importance in developing methods like this is high.

    The manuscript is overall ok. However, more data is needed to convince the reader that the method is robust enough to work with other samples in other labs. This would greatly improve the impact of the publication.

    Page 6.

    • How is the data acquired?

    Page 8.
    "This indicates that whileKimmelNet …