CDI-Type-II: Computational Challenges in the Discovery and Understanding of Complex Boiological Structures through Multimodal Imaging

CDI-Type-II：通过多模态成像发现和理解复杂生物结构的计算挑战

• 批准号: 0941717
• 负责人: Bangalore Manjunath
• 金额: $ 195万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0941717&HistoricalAwards=false
• 关键词: CDI; Type; II; Computational; Challenges

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Recent advances in imaging have enabled multimodal/multiscale observations of complex natural systems. Annotating, harvesting, extracting, and correlating the information contained in these vast image volumes is critically dependent on new information-processing tools as well as robust workflow implementation of well-established tools. In cases such as bioimaging, image data comes from different physical samples from different specimens and needs to be statistically harmonized. Though piecewise computational workflows in data collection are often highly automated, little progress has been made in effective and efficient knowledge discovery. The lack of processing and discovery tools to navigate data of such complexity and magnitude is a critical bottleneck.The project's computational efforts focus on a biological system that represents a unique combination of high complexity and accessibility for imaging: the vertebrate retina. The retina has a very complex yet highly structured architecture consisting of an unknown number of repeated neural circuits. Though it has been a focus of intense anatomical and physiological studies for over a century, no complete retinal map exists today. In fact, contrary to common misunderstanding, not even all of the retinal cell types have been discovered, much less mapped into functional circuitry. A retinal map is the critical anatomical ground truth that is needed in building realistic models of the earliest stage in visual processing. Building such a cell map requires advances in several areas, including imaging and molecular marker technologies, statistical pattern recognition, and databases. However, the critical barrier at this time is in analyzing the vast amount of images that will be generated from such a project, from samples coming from different retinal cross sections of different animals, and the need to integrate this information in a statistically robust manner to build a retinal map. It is expected that this project will advance not only the image-based information processing technologies but will also have a significant impact on neuroscience research.The project is interdisciplinary and brings together researchers at the University of Utah and UCSB. Students on the project will get a broad training in retinal neurobiology, computer science and electrical engineering. This project will integrate research and education by introducing the results of the research into courses taught by the PIs on image processing, databases, bioinformatics, and pattern recognition.

该奖项是根据2009年美国复苏和再投资法案(公法111-5)资助的。成像方面的最新进展使复杂自然系统的多模式/多尺度观测成为可能。注释、采集、提取和关联这些庞大的图像卷中包含的信息严重依赖于新的信息处理工具以及成熟工具的强大工作流程实施。在生物成像等情况下，图像数据来自不同样本的不同物理样本，需要进行统计协调。虽然数据收集中的分段计算工作流通常是高度自动化的，但在有效和高效的知识发现方面进展甚微。缺乏处理和发现工具来导航如此复杂和大量的数据是一个关键的瓶颈。该项目的计算努力集中在一个生物系统上，它代表了高复杂性和成像可访问性的独特组合：脊椎动物视网膜。视网膜有一个非常复杂但高度结构化的结构，由数量不详的重复神经回路组成。尽管一个多世纪以来，它一直是紧张的解剖学和生理学研究的焦点，但今天还没有完整的视网膜图存在。事实上，与普遍的误解相反，甚至还没有发现所有的视网膜细胞类型，更不用说映射到功能电路了。视网膜图是构建视觉处理最早阶段的逼真模型所需的关键解剖学基础事实。建立这样的细胞图谱需要在几个领域取得进展，包括成像和分子标记技术、统计模式识别和数据库。然而，目前的关键障碍是分析这样一个项目将产生的大量图像，这些图像来自不同动物的不同视网膜横断面样本，以及以统计稳健的方式整合这些信息以建立视网膜图的需要。预计该项目不仅将推动基于图像的信息处理技术的发展，还将对神经科学研究产生重大影响。该项目是跨学科的，汇集了犹他大学和UCSB的研究人员。该项目的学生将接受视网膜神经生物学、计算机科学和电气工程方面的广泛培训。该项目将把研究和教育结合起来，将研究成果引入私人投资机构教授的关于图像处理、数据库、生物信息学和模式识别的课程。