Intelligent Interfaces for Interactive Analysis of High-Content Cellular Images

描述(由申请人提供)：基于细胞的高内容筛选(HCS)最近导致了在各种外部处理下的基于图像的高通量细胞表型研究，例如化合物或RNA干扰(RNAi)。这些研究将极大地促进我们对基因功能的理解，揭示潜在的生物网络，并对癌症研究和药物发现/开发产生直接影响。然而，由于现有图像分析工具的不足，大多数HCS屏幕只依赖于简单的标记读数的分析，而没有探索细胞形态的最丰富和最深刻的方面。开发图像分析工具以有效和彻底地分析由HCS技术产生的高度多样化的细胞图像对于图像处理研究来说是相对较新的，领域知识还有待积累。尽管如此，建立领域知识需要人类专家在视觉上探索数量惊人的大量图像。因此，它呼吁一种新的计算范式，促进实验生物学家和计算生物学家之间的团队合作，以克服这一困境。我们建议开发一种新的计算范式，将无监督模式挖掘技术、可视化数据探索界面和基于内容的图像检索与相关反馈技术相结合，以促进HCS技术在生物医学研究中的应用。这一范例将被实现为一个名为imCellPhen的系统，它将在使用HCS技术的两个果蝇神经疾病模型的形态筛选的背景下进行评估和测试。ImCellPhen的主要特点是其智能界面，允许用户(A)有效和高效地导航大型HCS图像数据库，(B)可靠地检测新的细胞表型，以及(C)通过交互训练计算模型来教系统识别细胞表型。模型训练过程实际上是一个隐含的、无缝的、有效的领域知识积累过程。这项研究中开发的方案和技术将使任何HCS筛查受益，因此将成为生物医学研究社区的宝贵工具。

DESCRIPTION (provided by applicant): Cell-based High-Content Screening (HCS) has recently led to high-throughput image-based studies of cellular phenotypes under various external treatments such as chemical compound or or RNA interference (RNAi). Such studies will significantly advance our understanding of gene functions, shed new light on the underlying biological networks, and have direct impact on cancer research and drug discovery/development. However, due to the inadequacies of existing image analysis tools, most HCS screens only relied on analyses of simple marker readouts and left the most informative and profound aspects of cellular morphology unexplored. Domain knowledge is yet to be accumulated for developing image analysis tools to effectively and thoroughly analyze highly diverse cellular images generated by the HCS technology, which are relatively new to image processing research. Nonetheless, building up domain knowledge requires human experts to visually explore a prohibitively large number of images. Therefore, it calls for a new computing paradigm that facilitates teamwork between experimental and computational biologists to overcome this dilemma. We propose to develop a novel computing paradigm that integrates unsupervised pattern mining techniques, visual data exploration interfaces and content-based image retrieval with relevance feedback techniques to facilitate the application of the HCS technology to biomedical research. This paradigm will be realized as a system called imCellPhen, which will be evalutated and tested in the context of two morphological screens of Drosophila neurodisease models using the HCS technology. The main features of imCellPhen are its intelligent interfaces that allow users to (a) effectively and efficiently navigate large-scale HCS image databases, (b) reliably detect novel cellular phenotypes, and (c) teach the system to recognize cellular phenotypes by interactively training computational models. The model training procedure is in fact an implicit, seamless, and effective process for accumulating domain knowledge. The scheme and techniques developed in this research will benefit any HCS screens and thus will be valuable tools for the biomedical research community.