Title: Unsupervised image analysis for zebrafish embryogenesis using lab-on-a-chip embryo arrays

Authors: Kevin I-Kai Wang; Mark Andrews; Zoran Salcic; Alice Bates; Travis Scott; Jin Akagi; Donald Wlodkowic

Addresses: Department of Electrical and Computer Engineering, The University of Auckland, Private Bag 92019, Auckland Mail Centre, Auckland 1142, New Zealand ' Department of Electrical and Computer Engineering, The University of Auckland, Private Bag 92019, Auckland Mail Centre, Auckland 1142, New Zealand ' Department of Electrical and Computer Engineering, The University of Auckland, Private Bag 92019, Auckland Mail Centre, Auckland 1142, New Zealand ' Department of Electrical and Computer Engineering, The University of Auckland, Private Bag 92019, Auckland Mail Centre, Auckland 1142, New Zealand ' Department of Electrical and Computer Engineering, The University of Auckland, Private Bag 92019, Auckland Mail Centre, Auckland 1142, New Zealand ' School of Chemical Sciences, University of Auckland, 23 Symonds Street, 5F, Bldg. 301, Auckland 1142, New Zealand ' The OpenTech Factory, School of Applied Sciences, RMIT University, Bundoora West Campus, Building 223, Level 1, Room 32A, Plenty Road, P.O. Box 71, Bundoora, VIC 3083, Australia

Abstract: The zebrafish is a popular vertebrate model organism that is widely deployed with lab-on-a-chip (LoC) technology for in-situ experiments in drug discovery and eco-toxicity assays. Although LoC devices enable easy manipulation and trapping of embryos for such experiments, constant human attention is required during and after the experiments for online and offline monitoring and analysis. The throughput and turnaround time are both limited from lack of automated image analysis. In this paper, a novel image analysis algorithm is developed to automatically recognise dead embryos and the first two stages of the zebrafish embryo development in order to detect anomalies caused by an applied chemical agent during embryogenesis. The algorithm has been examined using 55 zebrafish embryo images and has achieved a success rate of 94.5% in recognising the correct embryo development stage.

Keywords: lab-on-a-chip; image analysis; fish embryo toxicity assay; unsupervised image analysis; zebrafish embryogenesis; LoC embryo arrays; dead embryo recognition; embryo images; embryo development stage.

DOI: 10.1504/IJCAT.2014.063912

International Journal of Computer Applications in Technology, 2014 Vol.50 No.1/2, pp.99 - 112

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 25 Jul 2014 *

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