Center for Open Bioimage Analysis

开放生物图像分析中心

• 批准号: 10061637
• 负责人: Anne E. Carpenter
• 金额: $ 35.82万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10061637
• 关键词: Active Learning; Address; Algorithms; Area; Automobile Driving; Benchmarking; Biological; Biological Models; Biological Process; Biology; Biomedical Research; Cell Nucleus; Cells; Cellular Structures; Cellular biology; Communities; Complement; Complex; Computational Technique; Computer Vision Systems; Computer software; Computers; Data; Data Set; Development; Diagnostic; Dimensions; Disease; Ensure; Event; Face; Feedback; Future; Image; Image Analysis; Institutes; International; Laboratories; Letters; Machine Learning; Measures; Methods; Microscopy; Mission; Modality; Modeling; Online Systems; Organism; Phenotype; Process; Reproducibility; Research; Research Personnel; Resource Sharing; Resources; Risk; Role; Scientist; Side; Software Engineering; Software Tools; Structure; Techniques; Technology; Time; Tissues; Training; Translating; Universities; Validation; Variant; Wisconsin; Work; advanced analytics; base; bioimaging; biological research; catalyst; deep learning; deep neural network; experience; experimental study; graphical user interface; image processing; imaging modality; improved; innovation; interest; learning strategy; light microscopy; microscopic imaging; neural network; next generation; open source; quantitative imaging; research and development; scale up; skills; software development; technology research and development; tool; user friendly software; user-friendly

Project Summary The Center for Open Bioimage Analysis will serve the cell biology community’s growing need for sophisticated software for light microscopy image analysis. Quantitative image analysis has become an indispensable tool for biologists using microscopy throughout basic biological and biomedical research. Quantifying images is now a critical, widespread need as imaging experiments continue to grow in scale, size, dimensionality, scope, modality, and complexity. Many biologists are missing out on the quantitative bioimaging revolution due to lack of effective algorithms and/or usable software for their needs, or lack of access to training. The Center brings together the Carpenter laboratory at the Broad Institute and the Eliceiri laboratory at the University of Wisconsin­Madison, and in doing so brings together the two most popular open source bioimage analysis projects, ImageJ (including ImageJ2 and FIJI) and CellProfiler. Through the collaborative development and dissemination of open source image analysis software, as well as training events and resources, the Center will empower thousands of researchers to apply advanced analytics in innovative ways to address new experimental areas. Building on the team’s expertise developing algorithms and user­friendly software for use in biology under real­world conditions, the Center will focus on two Technology Research and Development (TR&D) projects: deep learning­based image processing, and accessibility of image­processing algorithms for biologists. This work will not occur in isolation at the Center; rather, the Center will nucleate a larger community working on these two areas and serve as a catalyst and organizing force to create software and resources shared by all. The Driving Biological Projects (DBPs) will serve a major role in driving the TR&D work: our teams are accustomed to working deeply and iteratively on problems side by side and with frequent feedback from biologists. This will ensure that important cell biological problems drive the work of the Center. The DBPs reflect tremendous variety in terms of biological questions, model systems, imaging modalities, and researcher expertise and will ensure robustness of our tools for the widest possible impact on the community. Continuing the teams’ track record with ImageJ and CellProfiler, two mature open source bioimage analysis software projects critical to the work of biologists worldwide, the Center will also assist and train biologists in applying the latest computational techniques to important biological problems involving images. In short, the need for robust, accurate, and readily usable software is more urgent than ever. The Center for Open Bioimage Analysis will serve as a hub for pioneering new computational strategies for diverse biological problems, translating them into user­friendly software, further developing ImageJ and CellProfiler, and training the biological community to apply advanced software to important and diverse problems in cell biology.

项目概要 开放生物图像分析中心将满足细胞生物学界日益增长的需求 用于光学显微镜图像分析的复杂软件已成为一种定量图像分析。 生物学家在整个基础生物学和生物医学研究中使用显微镜的不可或缺的工具。 随着成像实验规模不断扩大，量化图像现在已成为一项关键且广泛的需求， 许多生物学家都忽略了大小、维度、范围、形态和复杂性。 由于缺乏有效的算法和/或可用的软件来满足其需求，或者缺乏 该中心汇集了布罗德研究所的卡彭特实验室和 Eliceiri。 威斯康星大学麦迪逊分校实验室，这样做汇集了两个最受欢迎的开放 来源生物图像分析项目，ImageJ（包括ImageJ2和FIJI）和CellProfiler。 开源图像分析软件的协作开发和传播以及培训 事件和资源，该中心将使数千名研究人员能够应用先进的分析 解决新实验领域的创新方法。 以团队的专业知识为基础，开发用于生物学的算法和用户友好的软件 根据实际情况，该中心将重点关注两个技术研发 (TR&D) 项目： 基于深度学习的图像处理，以及生物学家的图像处理算法的可访问性。 该中心不会孤立地开展工作；相反，该中心将聚集一个更大的社区来开展工作； 这两个领域成为创建所有人共享的软件和资源的催化剂和组织力量。 驱动生物项目 (DBP) 将在推动 TR&D 工作中发挥重要作用：我们的团队 习惯于并排深入、迭代地解决问题，并经常得到来自 这将确保重要的细胞生物学问题推动中心的工作。 反映了生物学问题、模型系统、成像方式和研究人员的巨大多样性 专业知识，并将确保我们的工具的稳健性，以持续对社区产生尽可能广泛的影响。 团队使用 ImageJ 和 CellProfiler（两种成熟的开源生物图像分析软件）的记录 对全世界生物学家的工作至关重要的项目，该中心还将协助和培训生物学家应用 涉及图像的重要生物学问题的最新计算技术。 简而言之，对强大、准确且易于使用的软件的需求比以往任何时候都更加迫切。 开放生物图像分析将作为针对不同生物开创新计算策略的中心 问题，将其转化为用户友好的软件，进一步开发 ImageJ 和 CellProfiler，以及培训 生物界将先进的软件应用于细胞生物学中重要且多样化的问题。