ABI: Innovation: Analyzing Neuroglial Cell Dynamics in their Natural Environment with Video Microscopy

ABI：创新：利用视频显微镜分析自然环境中的神经胶质细胞动力学

• 批准号: 1759802
• 负责人: Gustavo Rohde
• 金额: $ 62.93万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1759802&HistoricalAwards=false
• 关键词: ABI; Innovation; Analyzing; Neuroglial; Cell

Microscope videos of brain and nerve cells provide scientists unprecedented access to these cells as they develop, interact with each other, and respond to injury, disease, or other changes in their environment. However, these videos frequently contain dozens or hundreds of cells, behaving in complicated ways not easily discernable to a human viewer. This project hosted at the University of Virginia is innovating computer-based processing to pick out these cells and analyze their movements automatically. By simplifying these tasks, this software will permit studying large quantities of data for meaningful patterns and rules governing these cells' behavior. A front-end interface and back-end modules will enable scaling these capabilities from single videos to enormous databases, limited only by available computing power. These new capabilities will help scientists uncover new insights about these critical cells, leading researchers closer to understanding similarities and differences in cell behavior among animals used to study diseases and disorders that affect millions of Americans. By bringing together biologists and engineers, this project also provides exciting new experiences for students to learn about the future possibilities at the intersection of these fields. Training high-school teachers in image processing and its applications provides this well-rounded experience to even more students. To help neuroscientists observe the behavior of neurons and glia in their native setting, this software will automate the processing and analysis of complicated movements and interactions among dozens or hundreds of cells in high-resolution microscope videos. The interface will be both scalable and efficient, permitting rapid application of video enhancement, segmentation, and tracking software to large databases of microscope videos. The content-aware enhancement will suppress clutter while preserving cell features. Time-series segmentation will identify both cell bodies and ramified processes as they move between video frames and slices of the z-stack. Transport theory will enable tracking cell movements and other changes without having to construct a complicated model that could bias the results. Plugins under development for common packages like ImageJ and Vaa3D will allow scientists to integrate these tools with existing workflows. A modular design will facilitate expanding and refining the capabilities of this software over time. These software components can track how cells such as microglia and oligodendrocyte progenitor cells react to their environment and how these behaviors change during infection. Microscopy videos of mice, zebrafish, and other animal models will reveal insights not currently accessible due to the complicated behaviors of these cells. The collaborative nature of this project will provide valuable experiences for students embedded in the investigators? laboratories to learn more about image processing and biological applications. Training high school teachers on biological image processing during the summer will enable these teachers to share these experiences with students at schools across central Virginia and beyond. The software and research products will become available online at https://pages.shanti.virginia.edu/Neuroglia_Image_Toolkit/.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

大脑和神经细胞的显微镜视频为科学家提供了前所未有的机会，使他们能够在这些细胞发育、相互作用以及对伤害、疾病或环境中的其他变化做出反应时接触这些细胞。然而，这些视频通常包含数十个或数百个细胞，以人类观看者不容易辨别的复杂方式表现。这个由弗吉尼亚大学主持的项目正在创新基于计算机的处理，以挑选出这些细胞并自动分析它们的运动。通过简化这些任务，该软件将允许研究大量数据，以获得控制这些细胞行为的有意义的模式和规则。前端界面和后端模块将使这些功能从单个视频扩展到巨大的数据库，仅受可用计算能力的限制。这些新功能将帮助科学家发现关于这些关键细胞的新见解，使研究人员更接近于了解用于研究影响数百万美国人的疾病和病症的动物之间细胞行为的相似性和差异。通过将生物学家和工程师聚集在一起，该项目还为学生提供了令人兴奋的新体验，以了解这些领域交叉点的未来可能性。培训高中教师在图像处理及其应用提供了这种全面的经验，甚至更多的学生。 为了帮助神经科学家观察神经元和胶质细胞在其自然环境中的行为，该软件将自动处理和分析高分辨率显微镜视频中数十或数百个细胞之间的复杂运动和相互作用。该接口将是可扩展的和高效的，允许视频增强，分割和跟踪软件的大型显微镜视频数据库的快速应用。内容感知增强将抑制混乱，同时保留细胞功能。时间序列分割将识别细胞体和分支过程，因为它们在视频帧和z堆栈的切片之间移动。传输理论将使跟踪细胞运动和其他变化成为可能，而不必构建一个复杂的模型，可能会导致结果的偏差。正在为ImageJ和Vaa3D等通用软件包开发的插件将允许科学家将这些工具与现有工作流程集成。模块化设计将有助于随着时间的推移扩展和完善该软件的功能。这些软件组件可以跟踪小胶质细胞和少突胶质细胞祖细胞等细胞如何对其环境作出反应，以及这些行为在感染期间如何变化。小鼠、斑马鱼和其他动物模型的显微镜视频将揭示由于这些细胞的复杂行为而目前无法获得的见解。这个项目的合作性质将提供宝贵的经验，为学生嵌入在调查？实验室学习更多关于图像处理和生物应用。在夏季对高中教师进行生物图像处理培训，将使这些教师能够与弗吉尼亚州中部及其他地区的学生分享这些经验。该软件和研究产品将在www.example.com上在线提供https://pages.shanti.virginia.edu/Neuroglia_Image_Toolkit/.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

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• 期刊: Applied Sciences
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Content-Aware Enhancement of Images With Filamentous Structures

• DOI: 10.1109/tip.2019.2897289
• 发表时间: 2019-07-01
• 期刊: IEEE TRANSACTIONS ON IMAGE PROCESSING
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• 作者: Jeelani, Haris;Liang, Haoyi;Weller, Daniel S.
• 通讯作者: Weller, Daniel S.

Image segmentation for neuroscience: lymphatics

• DOI: 10.1088/2515-7647/ac050e
• 发表时间: 2021
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Complexity Analysis and u-net Based Segmentation of Meningeal Lymphatic Vessels

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• DOI: 10.1109/ieeeconf51394.2020.9443412
• 发表时间: 2020
• 期刊: Systems and Computers
• 作者: Tabassum, Nazia;Ferguson, Michael;Herz, Jasmin;Acton, Scott T.
• 通讯作者: Acton, Scott T.

Fast Automatic Parameter Selection for MRI Reconstruction

• DOI: 10.1109/isbi45749.2020.9098569
• 发表时间: 2020-04
• 期刊: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
• 作者: T. T. Toma-T.;D. Weller