Intelligent Interfaces for Interactive Analysis of High-Content Cellular Images

用于高内容细胞图像交互式分析的智能界面

• 批准号: 7470047
• 负责人: Pengyu Hong
• 金额: $ 17.31万
• 依托单位: BRANDEIS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-13 至 2010-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7470047
• 关键词: Alzheimer' s disease model; Biological; Biomedical Research; Cells; Cellular Morphology; Chemicals; Communities; Computer Simulation; Data; Data Analyses; Databases; Development; Disease model; Drosophila genus; Educational process of instructing; Environment; Facility Construction Funding Category; Feedback; Future; Gene Expression; Genes; Genome; Goals; Human; Huntington Disease; Image; Image Analysis; Knowledge; Left; Light; Metadata; Methods; Mining; Modeling; Neurons; Numbers; Pattern; Phenotype; Preclinical Drug Evaluation; Procedures; Process; RNA Interference; Recording of previous events; Research; Research Personnel; Retrieval; Scheme; Scientist; Screening procedure; System; Techniques; Technology; Testing; Training; User-Computer Interface; Visual; Week; anticancer research; base; cell type; cellular imaging; day; desire; detector; drug discovery; experience; gene function; high throughput technology; image processing; innovation; interest; novel; programs; research study; tool

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.

描述（由申请人提供）：基于细胞的高含量筛选（HCS）最近导致了各种外部处理（如化合物或RNA干扰（RNAi））下细胞表型的高通量基于图像的研究。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屏幕仅依赖于简单的标记读数分析，而没有探索细胞形态学中最具信息量和最深刻的方面。为了开发图像分析工具来有效和彻底地分析由HCS技术生成的高度多样化的细胞图像，还需要积累领域知识，这对图像处理研究来说是相对较新的。尽管如此，建立领域知识需要人类专家从视觉上探索大量的图像。因此，它需要一种新的计算范式，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，该系统将在使用HCS技术的果蝇神经疾病模型的两种形态学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)有效和高效地浏览大规模HCS图像数据库，(b)可靠地检测新的细胞表型，以及(c)通过交互式训练计算模型教系统识别细胞表型。模型训练过程实际上是一个隐式的、无缝的、有效的领域知识积累过程。本研究开发的方案和技术将有利于任何HCS筛选，因此将成为生物医学研究界的宝贵工具。