ZFIN ID: ZDB-PUB-150113-13
Phenotype classification of zebrafish embryos by supervised learning
Jeanray, N., Marée, R., Pruvot, B., Stern, O., Geurts, P., Wehenkel, L., Muller, M.
Date: 2015
Source: PLoS One 10: e0116989 (Journal)
Registered Authors: Muller, Marc
Keywords: none
MeSH Terms:
  • Amiodarone/pharmacology
  • Animals
  • Automation
  • Embryo, Nonmammalian/drug effects
  • Embryo, Nonmammalian/physiology
  • Image Processing, Computer-Assisted
  • Larva/drug effects
  • Larva/physiology
  • Machine Learning
  • Phenotype
  • Propranolol/pharmacology
  • Zebrafish/growth & development
  • Zebrafish/physiology*
PubMed: 25574849 Full text @ PLoS One
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.