High throughput automated detection of axial malformations in fish embryo
In
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 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.