An automated assay for the assessment of cardiac arrest in fish embryo

Abstract Studies on fish embryo models are widely developed in research. They are used in several research field such as drug discovery or environmental toxicology. In this article, we propose an entirely automated assay to detect cardiac arrest in Medaka ( Oryzias latipes ) based on image analysis....

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Bibliographic Details
Published in:Computers in biology and medicine 2017-02, Vol.81, p.32-44
Main Authors: Puybareau, Elodie, Genest, Diane, Barbeau, Emilie, Léonard, Marc, Talbot, Hugues
Format: Article
Language:English
Subjects:
Animals
Automation
Cameras
Cardiac arrest
Change detection
Computer Science
Computer Vision and Pattern Recognition
Connected filtering
Datasets
Ecotoxicology
Eggs
Embryos
Experts
Feasibility studies
Fetal Heart - diagnostic imaging
Fetal Heart - pathology
Heart Arrest - diagnostic imaging
Heart Arrest - embryology
Heart Arrest - pathology
Image Interpretation, Computer-Assisted - methods
Image Processing
Image stabilization
Internal Medicine
Life Sciences
Machine Learning
Medaka
Morphology
Oryzias - embryology
Oryzias latipes
Other
Pattern Recognition, Automated - methods
Photography - methods
Reproducibility of Results
Segmentation
Sensitivity and Specificity
Toxicology
Zebrafish
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Summary:Abstract Studies on fish embryo models are widely developed in research. They are used in several research field such as drug discovery or environmental toxicology. In this article, we propose an entirely automated assay to detect cardiac arrest in Medaka ( Oryzias latipes ) based on image analysis. We propose a multi-scale pipeline based on mathematical morphology. Starting from video sequences of entire wells in 24-well plates, we focus on the embryo, detect its heart, and ascertain whether or not the heart is beating based on intensity variation analysis. Our image analysis pipeline only uses commonly available operators. It has a low computational cost, allowing analysis at the same rate as acquisition. From an initial dataset of 3,192 videos, 660 were discarded as unusable (20.7%), 655 of them correctly so (99.25%) and only 5 incorrectly so (0.75%). The 2,532 remaining videos were used for our test. On these, 45 errors were made, leading to a success rate of 98.23%.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2016.12.007