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.

随着共焦、双光子和光片显微镜的日益普及和易用性， 荧光蛋白报告基因的快速发展使得3D和功能成像及其分析成为可能， 现代神经科学研究的核心组成部分。然而，获取3D和功能图像的容易性是 创建越来越大的数据集，促进了对高通量图像分析算法的需求， 既快速又准确的软件。虽然分析单个时间点图像的软件已经收到了 但是，目前缺乏用于分析多个时间点纵向成像数据集的工具。这 缺乏纵向图像分析工具是科学探究的主要障碍， 自己的分析策略，造成他人难以验证和复制此分析的情况。 所需要的是一个社区同意的纵向图像分析标准，促进共享。 在这里，我们建议开发软件来创建和策划纵向成像数据集的注释。 这个软件将解决一个主要问题，提供所需的严谨性和可重复性，同时使它容易 让研究人员分发他们的数据和分析。使这些重要的数据集变得可查找、可访问， 可互操作和可重用。为了实现这些目标，我们建议建立直观的Web浏览器和桌面 图形用户界面（GUI）将与基于云的数据和分析一起工作。这些GUI将被驱动 通过可编写脚本的Python高级编程接口（API）。为了在线编辑和分享，我们将 与BRAIN资助的Brain Image Library（BIL）合作，并与Neurodata Without 边界（NWB）和神经科学数据接口。我们将利用BRAIN Initiative NeuroMorpho.Org 和定义我们的研究方法（DORY），以确保我们的形态，连接， 并且生理签名包括可接受的元数据术语和词汇。 我们将与一组“种子”BRAIN资助的实验室密切合作，以获得反馈， 前端GUI和后端API的功能和可用性的改进。这将是 通过在线论坛、网站访问和加州大学戴维斯分校举办的黑客通村来实现。在新冠肺炎疫情期间， 我了解到，这些活动在虚拟环境中进行时效果非常好，并准备继续这样做 模型我们致力于为Web浏览器、桌面GUI和 Python API以及不断完善和简单的基于Web的交互式用例。到 为了确保社区的采用和使用，该提案还包括与一些“种子”实验室合作， 他们的数据通过整个管道从分析到在线共享。 长期目标是让Map Manager成为数据分析、探索和共享的催化剂。 有效地创建基于社区的方法，类似于其他学科，如天文学，其中数据是 广泛和公开共享，允许有效的数据挖掘和模型构建，以推动新的发现。