School of Engineering, RMIT University, Melbourne, 3000, Australia.
Faculty of Engineering & Information Technology, University of Technology Sydney, 2007, Sydney, Australia.
Sci Rep. 2017 Dec 14;7(1):17596. doi: 10.1038/s41598-017-17894-x.
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.
准确跟踪斑马鱼幼体的运动对于许多生物医学、药物和行为科学应用的研究至关重要。然而，斑马鱼幼体的运动特征与成年斑马鱼有很大的不同，现有的成年斑马鱼跟踪系统无法可靠地跟踪斑马鱼幼体。此外，幼体与容器之间的尺寸差异非常小，这不可避免地导致了水中杂质的检测，这进一步影响了斑马鱼幼体的跟踪，或者需要非常严格的视频成像条件，这通常会导致对现实实验条件的跟踪结果不可靠。本文研究了先进的计算机视觉分割技术和多目标跟踪算法的适应性，以开发一种准确、高效和可靠的多斑马鱼幼体跟踪系统。该系统已经在一系列单条和多条成年和幼体斑马鱼的视频上进行了测试，涵盖了各种（复杂）视频条件，包括阴影、标签、水气泡和背景伪影。与现有的最先进的和商业的多生物跟踪系统相比，该系统在无约束的视频成像条件下，将跟踪精度提高了 31.57%。为了便于对斑马鱼分割和跟踪研究进行评估，还提供了一个带有注释地面实况的数据集。该软件也可以公开获取。
