Automated staging of zebrafish embryos with deep learning

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 with reference to staging guides, which is both time-consuming and subjective. We recently reported a machine learning-based classifier, capable of quantifying the developmental delay between two populations of zebrafish embryos. Here, we build on that work by introducing a deep learning-based model that has been trained to predict the age (hours post fertilisation) of populations of zebrafish embryos. We show that when KimmelNet is tested on 2D brightfield im

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PROVIDER: S-BIAD840 | bioimages |

REPOSITORIES: bioimages