Key laboratory of Biomedical Engineering, of Ministry of Education Zhejiang University, Hangzhou, China.
J Neurosci Methods. 2013 Feb 15;213(1):6-13. doi: 10.1016/j.jneumeth.2012.11.009. Epub 2012 Dec 7.
The zebrafish (Danio rerio) has been widely used as a vertebrate animal model in neurobiological. The zebrafish has several unique advantages that make it well suited for live microscopic imaging, including its fast development, large transparent embryos that develop outside the mother, and the availability of a large selection of mutant strains. As the genome of zebrafish has been fully sequenced it is comparatively easier to carry out large scale forward genetic screening in zebrafish to investigate relevant human diseases, from neurological disorders like epilepsy, Alzheimer's disease, and Parkinson's disease to other conditions, such as polycystic kidney disease and cancer. All of these factors contribute to an increasing number of microscopic images of zebrafish that require advanced image processing methods to objectively, quantitatively, and quickly analyze the image dataset. In this review, we discuss the development of image analysis and quantification techniques as applied to zebrafish images, with the emphasis on phenotype evaluation, neuronal structure quantification, vascular structure reconstruction, and behavioral monitoring. Zebrafish image analysis is continually developing, and new types of images generated from a wide variety of biological experiments provide the dataset and foundation for the future development of image processing algorithms.
斑马鱼（Danio rerio）已被广泛用作神经生物学的脊椎动物模型。斑马鱼具有几个独特的优势，使其非常适合活体显微镜成像，包括其快速发育、在母体外发育的大透明胚胎，以及大量可供选择的突变株。由于斑马鱼的基因组已经完全测序，因此在斑马鱼中进行大规模正向遗传筛选以研究相关人类疾病变得相对容易，这些疾病包括癫痫、阿尔茨海默病和帕金森病等神经紊乱，以及多囊肾病和癌症等其他疾病。所有这些因素都导致需要先进的图像处理方法来客观、定量和快速分析图像数据集的斑马鱼微观图像数量不断增加。在这篇综述中，我们讨论了应用于斑马鱼图像的图像分析和量化技术的发展，重点是表型评估、神经元结构量化、血管结构重建和行为监测。斑马鱼图像分析在不断发展，并且来自各种生物实验的新型图像为图像处理算法的未来发展提供了数据集和基础。
