Map Manager: Longitudinal image analysis with online editing and sharing.

地图管理器：纵向图像分析，在线编辑和共享。

• 批准号: 10365810
• 负责人: Robert Harry Cudmore
• 金额: $ 115.85万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10365810
• 关键词: 3-Dimensional; 4D Imaging; Adoption; Algorithmic Software; Astronomy; Attention; Axon; BRAIN initiative; Back; Brain imaging; COVID-19 pandemic; Cells; Client; Collaborations; Communities; Computer software; Coupled; Data; Data Analyses; Data Set; Dendrites; Dendritic Spines; Development; Discipline; Documentation; Ecosystem; Encapsulated; Ensure; Environment; Event; Evolution; FAIR principles; Feedback; Fostering; Foundations; Functional Imaging; Funding; Goals; Image; Image Analysis; Individual; Internet; Intuition; Kinetics; Libraries; Light; Link; Machine Learning; Maps; Metadata; Microscope; Modeling; Modernization; Morphology; Neurons; Neurosciences; Neurosciences Research; Nomenclature; Online Systems; Parents; Physiological; Proteins; Publishing; Pythons; Recipe; Reporter; Reproducibility; Research Methodology; Research Personnel; Running; Schedule; Scientific Inquiry; Seeds; Series; Site Visit; Software Design; Software Tools; Structure; System; Three-Dimensional Image; Three-Dimensional Imaging; Time; Vertebral column; Visual; Visualization; Vocabulary; Work; analysis pipeline; automated algorithm; base; catalyst; cell type; cloud based; data mining; data modeling; data sharing; density; design; file format; flexibility; graphical user interface; high resolution imaging; image archival system; interest; interoperability; large datasets; longitudinal analysis; longitudinal design; model building; online repository; programs; serial imaging; software development; tool; two-photon; usability; virtual; web interface

The increasing availability and ease of use of confocal, two-photon, and light-sheet microscopes coupled with rapid developments in fluorescent protein reporters have made 3D and functional imaging and its analysis a central component of modern Neuroscience research. Yet, the ease of acquiring 3D and functional images is creating progressively larger datasets, prompting the need for high-throughput image analysis algorithms and software that can be both rapid and accurate. Although software to analyze single time-point images has received substantial attention, tools to analyze multiple time-point longitudinal imaging datasets is currently lacking. This lack of longitudinal image analysis tools is a major barrier to scientific inquiry with individual labs devising their own analysis strategies creating a situation where it is difficult for others to verify and reproduce this analysis. What is needed is a community agreed upon longitudinal image analysis standard that promotes sharing. Here, we propose to develop software to create and curate annotations in longitudinal imaging datasets. This software will solve a major problem by providing the needed rigor and reproducibility while making it easy for researchers to distribute their data and analysis. Making these important datasets findable, accessible, interoperable, and reusable. To achieve these goals, we propose to build intuitive web-browser and desktop graphical-user-interfaces (GUIs) that will work with cloud based data and analysis. These GUIs will be driven by a Python advanced-programming-interface (API) that is scriptable. For online editing and sharing we will work with the BRAIN funded Brain Image Library (BIL), and for interoperability with Neurodata Without Borders (NWB) and Neuroscience Data Interface. We will utilize the BRAIN Initiative NeuroMorpho.Org and Defining Our Research Methodology (DORY), to ensure our annotations of morphology, connectivity, and physiological signatures include accepted meta-data nomenclatures and vocabularies. We will work closely with a group of "seed" BRAIN funded labs to obtain feedback and make rapid improvements in the functionality and usability of the front-end GUIs and the back-end API. This will be achieved by online forums, site visits, and a hack-a-thon hosted at UC Davis. During the Covid pandemic we have learned that these events work extremely well when done virtually and are prepared to continue this model. We are committed to providing thorough documentation for the web-browser, desktop GUIs, and Python API as well as constantly refined and simple to follow recipes with interactive web-based use cases. To ensure community adoption and use, this proposal also includes working with a number of "seed" labs to run their data through the entire pipeline from analysis to online sharing. The long range goal is to have Map Manager act as a catalyst for data analysis, exploration, and sharing. Effectively creating a community based approach, akin to other disciplines such as astronomy, where data is widely and publicly shared allowing effective data mining and model building to advance new discoveries.

共聚焦，两光子和灯页显微镜的可用性和易于使用的易用性 荧光蛋白报道器的快速发展已经制造了3D和功能成像及其分析A 现代神经科学研究的中心部分。然而，获取3D和功能图像的便利性是 逐步创建更大的数据集，促使需要高通量图像分析算法和 可以既快速又准确的软件。尽管已收到了分析单个时间点图像的软件 目前缺乏分析多个时间点纵向成像数据集的大量关注。这 缺乏纵向图像分析工具是科学询问的主要障碍，个人实验室设计了他们 自己的分析策略创造了一种情况，使他人很难验证和复制此分析。 需要的是一个社区达成了促进共享的纵向图像分析标准。 在这里，我们建议开发软件，以在纵向成像数据集中创建和策划注释。 该软件将通过提供所需的严格性和可重复性，同时使其变得轻松，从而解决一个主要问题 供研究人员分发他们的数据和分析。使这些重要的数据集可找到，可访问， 可互操作，可重复使用。为了实现这些目标，我们建议建立直观的网络浏览器和桌面 将与基于云的数据和分析一起使用的图形 - 用户间隙（GUI）。这些Guis将被驱动 通过Python高级编程接口（API）。对于在线编辑和分享，我们将 与大脑资助的大脑图像库（BIL）一起工作，并与没有神经舞的互操作性 边界（NWB）和神经科学数据接口。我们将利用大脑倡议neuromorpho.org 并定义我们的研究方法（Dory），以确保我们对形态，连通性的注释 生理特征包括公认的元数据命名和词汇。 我们将与一组“种子”大脑资助的实验室紧密合作，以获得反馈并快速 前端GUI和后端API的功能和可用性的改善。这将是 通过在线论坛，现场访问和在加州大学戴维斯分校举办的骇人听闻的攻击。在COVID大流行期间 已经了解到，这些事件在虚拟完成后运作良好，并准备继续这样做 模型。我们致力于为网络浏览器，桌面GUI和 Python API以及不断完善且易于遵循与基于Web的用例的食谱。到 确保社区采用和使用，该建议还包括与许多“种子”实验室合作 他们的数据通过从分析到在线共享的整个管道。 远距离目标是让Map Manager作为数据分析，探索和共享的催化剂。 有效地创建一种基于社区的方法，类似于其他学科，例如天文学，数据是数据 广泛和公开共享，允许有效的数据挖掘和模型构建以推进新发现。