Reference : Phenotype Classification of Zebrafish Embryos by Supervised Learning
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
http://hdl.handle.net/2268/178357
Phenotype Classification of Zebrafish Embryos by Supervised Learning
English
Jeanray, Nathalie mailto [Université de Liège - ULg > Département des sciences de la vie > GIGA-R : Biologie et génétique moléculaire >]
Marée, Raphaël mailto [Université de Liège - ULg > > GIGA-Research >]
Pruvot, Benoist [> >]
Stern, Olivier mailto [Université de Liège - ULg > > SEGI : ULIS : Logique métier >]
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Muller, Marc mailto [Université de Liège - ULg > Département des sciences de la vie > GIGA-R : Biologie et génétique moléculaire >]
2015
PLoS ONE
Public Library of Science
10
1
e0116989, 1-20
Yes (verified by ORBi)
International
1932-6203
San Franscisco
CA
[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.
Giga-Development and Stem Cells ; Applied and Fundamental FISH Research Center - AFFISH-RC ; Centre Interfacultaire d'Analyse des Résidus en Traces - CART
http://hdl.handle.net/2268/178357
10.1371/journal.pone.0116989
http://dx.plos.org/10.1371/journal.pone.0116989

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