Reference : Automatic localization of interest points in zebrafish images with tree-based methods
Scientific congresses and symposiums : Paper published in a book
Life sciences : Biochemistry, biophysics & molecular biology
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/99199
Automatic localization of interest points in zebrafish images with tree-based methods
English
Stern, Olivier mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Marée, Raphaël mailto [Université de Liège - ULg > > GIGA-Management : Plateforme bioinformatique >]
Aceto, Jessica mailto [Université de Liège - ULg > Département des sciences de la vie > GIGA-R : Biologie et génétique moléculaire >]
Jeanray, Nathalie 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 >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2011
Proceedings of the 6th IAPR International Conference on Pattern Recognition in Bioinformatics
Springer
LNCS
Yes
No
International
6th IAPR International Conference on Pattern Recognition in Bioinformatics (2011)
November 2-4 2011
Delft University of Technology
Delft
The Netherlands
[en] In many biological studies, scientists assess effects of experimental conditions by visual inspection of microscopy images. They are able to observe whether a protein is expressed or not, if cells are going through normal cell cycles, how organisms evolve in different experimental conditions, etc. But, with the large number of images acquired in high-throughput experiments, this manual inspection becomes lengthy, tedious and error-prone. In this paper, we propose to automatically detect specific interest points in microscopy images using machine learning methods with the aim of performing automatic morphometric measurements in the context of Zebrafish studies. We systematically evaluate variants of ensembles of classification and regression trees on four datasets corresponding to different imaging modalities and experimental conditions. Our results show that all variants are effective, with a slight advantage for multiple output methods, which are more robust to parameter choices.
Giga-Systems Biology and Chemical Biology ; Giga-Development and Stem Cells
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS ; Politique Scientifique Fédérale (Belgique) = Belgian Federal Science Policy ; Région wallonne : Direction générale des Technologies, de la Recherche et de l'Energie - DGTRE ; Fonds Européen de Développement Régional - FEDER ; Action de Recherche Concertée BIOMOD (Université de Liège)
http://hdl.handle.net/2268/99199

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