Noncontact assessment of heart rate (HR) in Zebrafish larvae, based on a video record of the organism, acquired using a camera mounted on a microscope, has gained enormous significance. Completely automatic and robust estimation of HR from videos of nontransgenic larvae requires the determination of an appropriate region of interest (ROI), followed by suitable signal processing steps. Toward such a goal, we develop a fully automatic and adaptive ROI enclosing a predominant portion of the beating heart, irrespective of the image resolution and zoom. The information within the ROI is used to get one or more time series, to be processed for extracting the signal containing information about the beating heart. Among the various possibilities, we show that the multichannel approach exploiting color information and based on independent component analysis to extract the cardiac signal—is desirable, due to several reasons, including its ability to handle noise, minor movements of the larvae or of the platform, and statistical performance. The proposed sequence of algorithms is validated on videos of 41 larvae (2 days and 4 days postfertilization). The computer estimated values of HR compared well with the ground truth obtained by visual-counting. We have also devised a method of tracking the ROI associated with drifting larvae and tested it on real data. In addition, an example of handling a type of arrhythmia is given. Note to Practitioners—Studying the Zebrafish larva as a model organism, is very useful in developmental biology, toxicology, and pharmacological testing—due to the similarity in the response to various drugs, easiness of breeding, high throughput, lesser cost involved, in vivo testing, and reduced bioethical concerns. The traditional approach of manually counting the heart beat of the larvae kept under a microscope is tedious. Automating the process using videos of the larvae placed under a microscope is therefore important. The existing approaches are either not completely automatic or involve expensive equipment and/or modifying the larvae (biological intervention) such that the heart expresses fluorescence protein, and are not designed to handle even minor motion. We consider completely automating the process of estimating the heart rate from nontransgenic zebrafish larvae—avoiding any kind of manual intervention. The solution to the preceding lies in automating: 1) the determination of the ROI that adapts to the location and size of the larvae, camera zoom, and image resolution; 2) the tracking of the ROI to handle drifting larvae; and 3) estimation of HR through appropriate signal processing, to add to the robustness of the method, including the handling of mild motion. We present a completely automatic and robust HR estimation algorithm, which works automatically to estimate an adaptive ROI, followed by independent component analysis. The method works even in the presence of minor motion of the larvae, including drift. We plan to extend this paper to studying heart rate variability—an important method of quantifying the neural control of the cardiovascular system as well as identifying several pathologies—and certain types of arrhythmia.

中文翻译：

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斑马鱼幼虫心率的自动和鲁棒估计

基于安装在显微镜上的摄像头获取的有机体的视频记录，对斑马鱼幼虫的心率（HR）进行非接触式评估具有重要的意义。要从非转基因幼虫的视频中对HR进行全自动且鲁棒的估计，需要确定适当的目标区域（ROI），然后进行适当的信号处理步骤。为了实现这一目标，我们开发了一种全自动的自适应ROI，将跳动的心脏的主要部分封闭起来，而与图像分辨率和缩放无关。ROI中的信息用于获取一个或多个时间序列，将其进行处理以提取包含有关搏动心脏的信息的信号。在各种可能性中，我们表明，由于多种原因，包括色彩处理能力，幼虫或平台的微小移动以及统计性能，采用彩色信息并基于独立成分分析提取心脏信号的多通道方法是理想的。在41个幼虫的视频（受精后2天和4天）上验证了算法的建议序列。计算机估算的HR值与通过视觉计数获得的基本事实进行了很好的比较。我们还设计了一种跟踪与漂移幼虫相关的ROI的方法，并在真实数据上对其进行了测试。另外，给出了处理一种类型的心律不齐的例子。给从业者的注意-研究斑马鱼幼虫作为模型生物，在发育生物学，毒理学，和药理测试-由于对各种药物的反应相似，易于育种，高通量，涉及的成本更低，体内测试和减少的生物伦理学顾虑。手动计数显微镜下保存的幼虫心跳的传统方法很繁琐。因此，使用放置在显微镜下的幼虫视频自动进行处理非常重要。现有的方法不是完全自动的或涉及昂贵的设备和/或修饰幼虫（生物干预）以使心脏表达荧光蛋白，并且没有被设计来处理甚至很小的运动。我们考虑完全自动化非转基因斑马鱼幼虫估算心率的过程-避免任何手动干预。前面的解决方案在于自动化：1）确定适合幼虫的位置和大小，相机变焦和图像分辨率的ROI；2）跟踪ROI以处理幼虫漂移；和3）通过适当的信号处理来估算HR，以增加方法的鲁棒性，包括对轻微运动的处理。我们提出了一种全自动且健壮的HR估算算法，该算法可自动估算自适应的ROI，然后进行独立的成分分析。该方法即使在幼虫出现轻微运动（包括漂移）的情况下也可以使用。我们计划将本文扩展到研究心率变异性（一种量化心血管系统神经控制以及确定几种病理状态的重要方法）和某些类型的心律失常。