View source for Publications/puybareau.18.fish

{{Publication
| published = true
| date = 2018-11-29
| authors = Diane Genest, Elodie Puybareau, Jean Cousty, Marc Leonard, Hugues Talbot, Noemie De Croze
| title = High throughput automated detection of axial malformations in fish embryo
| howpublished = Communication at the 5th international symposium and workshop on Fish and amphibian embryos as alternative models in toxicology and teratology
| abstract = Fish embryo models are widely used as screening tools to assess the efficacy and /or toxicity of chemicals. This assessment involves analysing embryo morphological abnormalities. In this article, we propose a multi-scale pipeline to allow automated classification of fish embryos (Medaka: Oryzias latipes) based on the presence or absence of spine malformations. The proposed pipeline relies on the acquisition of fish embryo 2D images, on feature extraction due to mathematical morphology operators and on machine learning classification. After image acquisitionsegmentation tools are used to focus on the embryo before analysing several morphological features. An approach based on machine learning is then applied to these features to automatically classify embryos according to the detection of axial malformations. We built and validated our learning model on 1,459 images with a 10-fold cross- validation by comparison with the gold standard of 3D observations performed under a microscope by a trained operator. Our pipeline results in correct classification in 85% of the cases included in the database. This percentage is similar to the percentage of success of a trained human operator working on 2D images. Indeed, most of the errors are due to the inherent limitations of 2D images compared to 3D observations. The key benefit of our approach is the low computational cost of our image analysis pipeline, which guarantees optimal throughput analysis.
| lrdeprojects = Olena
| lrdekeywords = Image
| lrdenewsdate = 2018-11-29
| type = misc
| id = puybareau.18.fish
| bibtex = 
@Misc<nowiki>{</nowiki>		  puybareau.18.fish,
  author	= <nowiki>{</nowiki>Diane Genest and Elodie Puybareau and Jean Cousty and Marc
		  Leonard and Hugues Talbot and Noemie De Croze<nowiki>}</nowiki>,
  title		= <nowiki>{</nowiki>High throughput automated detection of axial malformations
		  in fish embryo<nowiki>}</nowiki>,
  howpublished	= <nowiki>{</nowiki>Communication at the 5th international symposium and
		  workshop on Fish and amphibian embryos as alternative
		  models in toxicology and teratology<nowiki>}</nowiki>,
  month		= nov,
  year		= <nowiki>{</nowiki>2018<nowiki>}</nowiki>,
  abstract	= <nowiki>{</nowiki>Fish embryo models are widely used as screening tools to
		  assess the efficacy and /or toxicity of chemicals. This
		  assessment involves analysing embryo morphological
		  abnormalities. In this article, we propose a multi-scale
		  pipeline to allow automated classification of fish embryos
		  (Medaka: Oryzias latipes) based on the presence or absence
		  of spine malformations. The proposed pipeline relies on the
		  acquisition of fish embryo 2D images, on feature extraction
		  due to mathematical morphology operators and on machine
		  learning classification. After image acquisition,
		  segmentation tools are used to focus on the embryo before
		  analysing several morphological features. An approach based
		  on machine learning is then applied to these features to
		  automatically classify embryos according to the detection
		  of axial malformations. We built and validated our learning
		  model on 1,459 images with a 10-fold cross- validation by
		  comparison with the gold standard of 3D observations
		  performed under a microscope by a trained operator. Our
		  pipeline results in correct classification in 85\% of the
		  cases included in the database. This percentage is similar
		  to the percentage of success of a trained human operator
		  working on 2D images. Indeed, most of the errors are due to
		  the inherent limitations of 2D images compared to 3D
		  observations. The key benefit of our approach is the low
		  computational cost of our image analysis pipeline, which
		  guarantees optimal throughput analysis.<nowiki>}</nowiki>
<nowiki>}</nowiki>

}}