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Abstract
This paper introduces an automated deep learning approach using Twin Networks to analyze embryonic development. By calculating similarities between embryo images at different time points, the method generates phenotypic fingerprints reflecting developmental time and tempo. This allows for accurate embryo staging, quantification of temperature-dependent developmental tempo, detection of developmental abnormalities, and *de novo* generation of staging atlases for various species. The approach offers an objective and standardized way to analyze early embryogenesis.
Publisher
Nature Methods
Published On
Nov 23, 2023
Authors
Nikan Toulany, Hernán Morales-Navarrete, Daniel Čapek, Jannis Grathwohl, Murat Ünalan, Patrick Müller
Tags
automated deep learning
embryonic development
Twin Networks
phenotypic fingerprints
embryo staging
developmental tempo
developmental abnormalities

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