[en] 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.
Research Center/Unit :
Giga-Development and Stem Cells - ULiège AFFISH-RC - Applied and Fundamental FISH Research Center - ULiège CART - Centre Interfacultaire d'Analyse des Résidus en Traces - ULiège
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
Jeanray, Nathalie ; Université de Liège - ULiège > Département des sciences de la vie > GIGA-R : Biologie et génétique moléculaire
Marée, Raphaël ; Université de Liège - ULiège > GIGA-Research
Pruvot, Benoist
Stern, Olivier ; Université de Liège - ULiège > SEGI : ULIS : Logique métier
Geurts, Pierre ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
Wehenkel, Louis ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Muller, Marc ; Université de Liège - ULiège > Département des sciences de la vie > GIGA-R : Biologie et génétique moléculaire
Language :
English
Title :
Phenotype Classification of Zebrafish Embryos by Supervised Learning
Publication date :
2015
Journal title :
PLoS ONE
eISSN :
1932-6203
Publisher :
Public Library of Science, San Franscisco, United States - California
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