title: Phenotype classification of zebrafish embryos by supervised learning.
abstract: Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.
authors.affiliation: GIGA-Development, Stem Cells and Regenerative Medicine, Organogenesis and Regeneration, University of Liège, Liège, Belgium; GIGA-Systems Biology and Chemical Biology, Dept. EE & CS, University of Liège, Liège, Belgium.
authors.forename: Nathalie
authors.initials: N
authors.lastname: Jeanray
authors.affiliation: GIGA Bioinformatics Core Facility, University of Liège, Liège, Belgium.
authors.forename: Raphaël
authors.initials: R
authors.lastname: Marée
authors.affiliation: GIGA-Development, Stem Cells and Regenerative Medicine, Organogenesis and Regeneration, University of Liège, Liège, Belgium.
authors.forename: Benoist
authors.initials: B
authors.lastname: Pruvot
authors.affiliation: GIGA-Systems Biology and Chemical Biology, Dept. EE & CS, University of Liège, Liège, Belgium.
authors.forename: Olivier
authors.initials: O
authors.lastname: Stern
authors.affiliation: GIGA-Systems Biology and Chemical Biology, Dept. EE & CS, University of Liège, Liège, Belgium.
authors.forename: Pierre
authors.initials: P
authors.lastname: Geurts
authors.affiliation: GIGA-Systems Biology and Chemical Biology, Dept. EE & CS, University of Liège, Liège, Belgium; GIGA Bioinformatics Core Facility, University of Liège, Liège, Belgium.
authors.forename: Louis
authors.initials: L
authors.lastname: Wehenkel
authors.affiliation: GIGA-Development, Stem Cells and Regenerative Medicine, Organogenesis and Regeneration, University of Liège, Liège, Belgium.
authors.forename: Marc
authors.initials: M
authors.lastname: Muller
mesh_terms: D000638:Amiodarone / Q000494:pharmacology; D000818:Animals; D001331:Automation; D004625:Embryo, Nonmammalian / Q000187:drug effects / Q000502:physiology; D007091:Image Processing, Computer-Assisted; D007814:Larva / Q000187:drug effects / Q000502:physiology; D000069550:Machine Learning; D010641:Phenotype; D011433:Propranolol / Q000494:pharmacology; D015027:Zebrafish / Q000254:growth & development / Q000502:physiology*
chemical_list: D011433:Propranolol; D000638:Amiodarone