{"database":"bioimages","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":[null],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-BIAD840"],"repository":["bioimages"],"figure_sub":["Specimen","Study Component","organisation","Biosample","Associations","Image acquisition"],"pubmed_authors":["Matthew J Renshaw","David Barry","Rebecca A Jones","Danelle Devenport"],"additional_accession":[]},"is_claimable":false,"name":"Automated staging of zebrafish embryos with deep learning","description":"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","dates":{"release":"2023-08-10T00:00:00Z","modification":"2023-08-10T14:29:46.991Z","creation":"2023-08-10T14:29:46.991Z"},"accession":"S-BIAD840","cross_references":{}}