SI2-SSI: LIMPID: Large-Scale IMage Processing Infrastructure Development

SI2-SSI：LIMPID：大规模图像处理基础设施开发

• 批准号: 1664172
• 负责人: Bangalore Manjunath
• 金额: $ 340万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1664172&HistoricalAwards=false
• 关键词: SI2; SSI; LIMPID; Large; Scale; SI2; SSI; LIMPID; Large; Scale

Scientific imaging is ubiquitous: From materials science, biology, neuroscience and brain connectomics, marine science and remote sensing, to medicine, much of the big data science is image centric. Currently, interpretation of images is usually performed within isolated research groups either manually or as workflows over narrowly defined conditions with specific datasets. This LIMPID (Large-scale IMage Processing Infrastructure Development) project will have a transformative impact on such discipline-centric workflows through the creation of an extensive and unique resource for the curation, distribution and sharing of scientific image analysis methods. The project will create an image processing marketplace for use by a diverse community of researchers, enabling them to discover, test, verify and refine image analysis methods within a shared infrastructure. As a freely available, cloud-based resource, LIMPID will facilitate participation of underrepresented groups and minority-serving institutions, as well as international scientists, allowing them to address questions that would otherwise require expensive software. The potential impacts of the project are significant: from wide dissemination of novel processing methods, to development of automatic methods that can leverage data and human feedback from large datasets for software training and validation. For the broader scientific community, this immediately provides a resource for joint data and methods publication, with provenance control and security. This in turn will facilitate faster development and deployment of tools and foster new collaborations between computer scientists developing methods and scientific users. The project will prepare a diverse cadre of students and researchers, including women and members of under-represented groups, to tackle complex problems in an interdisciplinary environment. Through workshops, participation at scientific meetings, and summer undergraduate research internships, a broad community of users will be engaged to actively contribute to all aspects of research, development, and training during the course of this project.  The primary goal is to create a large scale distributed image processing infrastructure, the LIMPID, though a broad,  interdisciplinary collaboration of researchers in databases, image analysis, and sciences.  In order to create a resource of broad appeal, the focus will be on three types of image processing: simple detection and labelling of objects based on detection of significant features and leveraging recent advances in deep learning, semi-custom pipelines and workflows based on popular image processing tools, and finally fully customizable analysis routines.  Popular image processing pipeline tools will be leveraged to allow users to create or customize existing pipeline workflows and easily test these on large-scale cloud infrastructure from their desktop or mobile devices. In addition, a core cloud-based platform will be created where custom image processing can be created, shared, modified, and executed on large-scale datasets and apply novel methods to minimize data movement. Usage test cases will be created for three specific user communities: materials science, marine science and neuroscience. An industry supported consortium will be established at the beginning of the project towards achieving long-term sustainability of the LIMPID infrastructure.This project is supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer & Information Science and Engineering and the Division of Materials Research in the Directorate for Mathematical and Physical Sciences.

科学成像无处不在：从材料科学，生物学，神经科学和脑连接组学，海洋科学和遥远敏感性到医学，大部分大数据科学都以图像为中心。当前，图像的解释通常是在孤立的研究组中手动或用特定数据集的狭义条件上的工作流进行的。这个Limpid（大规模图像处理基础架构开发）项目将通过创建用于课程，科学图像分析方法的课程，分发和共享的广泛而独特的资源，对以学科为中心的工作流产生变革性的影响。该项目将创建一个图像处理市场，以供研究人员的潜水员社区使用，使他们能够在共享基础架构中发现，测试，验证和完善图像分析方法。作为免费的，基于云的资源，Limpid将有助于促进代表性不足的团体和少数族裔服务机构的参与，以及国际科学家，使他们能够解决否则需要昂贵软件的问题。该项目的潜在影响是重要的：从新型处理方法的广泛传播到可以利用大型数据集中数据和人类反馈的自动方法进行软件培训和验证。对于更广泛的科学界而言，这立即提供了共同数据和方法出版物的资源，并提供了来源控制和安全性。反过来，这将有助于更快地开发和部署工具，并促进计算机科学家开发方法和科学用户之间的新合作。该项目将准备多样化的学生和研究人员，包括妇女和代表性不足的群体的成员，以解决跨学科环境中的复杂问题。通过讲习班，参加科学会议以及夏季的本科研究实习，将在本项目的过程中积极参与研究，开发和培训的各个方面。主要目标是创建一个大规模的分布式图像处理基础架构，即研究人员在数据库，图像分析和科学方面的广泛，跨学科的合作。为了创建广泛的外观资源，重点将放在三种类型的图像处理上：基于对重要特征的检测并利用基于流行的图像处理工具的深度学习，半定期管道和工作流程的最新进展，对对象进行了简单检测和标记，并利用了最终的可定制分析。流行的图像处理管道工具将被利用，以允许用户创建或自定义现有的管道工作流程，并轻松地从其台式机或移动设备上在大规模的云基础架构上测试它们。此外，将创建一个基于核心的云平台，可以在大规模数据集中创建，共享，修改和执行自定义图像处理，并应用新颖的方法以最小化数据移动。将为三个特定用户社区创建使用测试用例：材料科学，海洋科学和神经科学。项目开始时将建立一个由行业支持的财团，以实现Limpid基础设施的长期可持续性。该项目得到了计算机与信息科学与工程局的高级网络策略办公室以及数学和物理科学局的材料研究局的支持。