Automatic multiple zebrafish larvae tracking in unconstrained microscopic video conditions

Wang, X, Cheng, E, Burnett, I, Huang, Y and Wlodkowic, D 2017, 'Automatic multiple zebrafish larvae tracking in unconstrained microscopic video conditions', Scientific Reports, vol. 7, no. 1, pp. 1-8.


Document type: Journal Article
Collection: Journal Articles

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Title Automatic multiple zebrafish larvae tracking in unconstrained microscopic video conditions
Author(s) Wang, X
Cheng, E
Burnett, I
Huang, Y
Wlodkowic, D
Year 2017
Journal name Scientific Reports
Volume number 7
Issue number 1
Start page 1
End page 8
Total pages 8
Publisher Nature
Abstract The accurate tracking of zebrafish larvae movement is fundamental to research in many biomedical, pharmaceutical, and behavioral science applications. However, the locomotive characteristics of zebrafish larvae are significantly different from adult zebrafish, where existing adult zebrafish tracking systems cannot reliably track zebrafish larvae. Further, the far smaller size differentiation between larvae and the container render the detection of water impurities inevitable, which further affects the tracking of zebrafish larvae or require very strict video imaging conditions that typically result in unreliable tracking results for realistic experimental conditions. This paper investigates the adaptation of advanced computer vision segmentation techniques and multiple object tracking algorithms to develop an accurate, efficient and reliable multiple zebrafish larvae tracking system. The proposed system has been tested on a set of single and multiple adult and larvae zebrafish videos in a wide variety of (complex) video conditions, including shadowing, labels, water bubbles and background artifacts. Compared with existing state-of-the-art and commercial multiple organism tracking systems, the proposed system improves the tracking accuracy by up to 31.57% in unconstrained video imaging conditions. To facilitate the evaluation on zebrafish segmentation and tracking research, a dataset with annotated ground truth is also presented. The software is also publicly accessible.
Subject Animal Behaviour
Animal Physiology - Systems
Image Processing
Keyword(s) Mixture-Models
Segmentation
DOI - identifier 10.1038/s41598-017-17894-x
Copyright notice © 2017 The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0
ISSN 2045-2322
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