计算机视觉在线虫进食行为自动分析中的应用

秀丽隐杆线虫被广泛地用作研究基因与行为关系的绝佳模式生物.线虫的咽部神经元回路控制着复杂的进食行为.为了研究进食行为的分子机制,有必要对线虫进食行为表型分析鉴定.然而,目前为止,几乎所有的线虫进食行为表型鉴定都是通过人眼来判断.因为其泵入食物的肌肉运动频率高,该行为的分析是很困难而且效率低下的.为解决这个问题,我们设计了基于计算机视觉技术的自动化成像系统来高通量分析线虫进食行为表型.此成像系统对进食表型的检测准确率达到98%以上,并使得连续可靠地分析其表型细微变化成为可能.同时,在保证高准确率的前提下单位时间内分析数据的效率比人工分析提高了3倍.

Application of Computer Vision in The Automatic Analysis of Feeding Behavior in C. elegans

The nematode Caenorhabditis elegans has been widely used as a perfect model organism to study the relationship between genes and behavior. The pharyngeal microcircuit of the worm controls a complex feeding behavior. In order to study the molecular basis of this feeding behavior, it is necessary to identify subtle differences in feeding activity of the worm. However, most of the phenotype analyzing of feeding behavior is accomplished by human eyes. And it is a tough and poor efficiency job to analyze the fast pumping muscle of the worm. To help improving this problem, an automated system has been developed based on computer vision for the high-throughput analysis of the feeding behavior by virtue of a simple webcam. Our system enables the consistent and subtle analysis of C. elegans pumping recordings and the accuracy of pumping detection is up to 98%. Under this high accuracy, the time cost of the behavior analysis is cut down by 67% versus human manipulation.