Neuroinformatics platform using machine learning and content-based image retrieval for neuroscience image data

使用机器学习和基于内容的图像检索来检索神经科学图像数据的神经信息学平台

• 批准号: 10251140
• 负责人: Paul Angstman
• 金额: $ 74.72万
• 依托单位: MICROBRIGHTFIELD, LLC
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-17 至 2022-09-16
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10251140
• 关键词: 3-Dimensional; Address; Amygdaloid structure; Animal Model; Appearance; Archives; Biotechnology; Brain; Brain Diseases; Brain imaging; Chicago; Cloud Service; Collaborations; Communities; Complex; Computer software; Data; Data Aggregation; Data Files; Data Provenance; Data Set; Data Sources; Digital Imaging and Communications in Medicine; Dimensions; Fostering; Human; Image; Information Systems; Infrastructure; Institutes; Institution; Intelligence; Laboratories; Machine Learning; Manuals; Microscopic; Modeling; Modernization; Mus; National Institute of Mental Health; Neurologic; Neurosciences; Neurosciences Research; New York; Notification; Pharmacology; Phase; Prevention strategy; Privacy; Problem Solving; Production; Public Domains; Records; Regenerative Medicine; Reproducibility; Research Personnel; Research Project Grants; Research Subjects; Retrieval; Schools; Scientist; Secure; Semantics; Societies; Source; System; Technology; Testing; Universities; Validation; Visual; Work; application programming interface; base; cloud based; collaborative environment; data access; data format; data repository; data sharing; fighting; flexibility; hands-on learning; improved; innovation; interest; neuroimaging; neuroinformatics; neuropathology; neuropsychiatry; new technology; novel; prevent; prototype; research and development; stem cells; treatment strategy; two-dimensional; usability; virtual environment; web services; whole slide imaging

This project aims to develop NeuroManager™, an innovative neuroinformatics platform for advanced parsing, storing, aggregating, analyzing and sharing of complex neuroscience image data. A core technology that we will develop in NeuroManager will be Image Content Analysis for Retrieval Using Semantics (ICARUS), a novel, intelligent neuroimage curation system that will enable image retrieval based on visual appearance or by semantic concept. ICARUS will use machine learning applied to content-based image retrieval - (CBIR) to build and refine models that summarize microscopic and macroscopic image appearance and automatically assign semantic concepts to neuroimages. Neuroscience research generates extensive, multifaceted data that is considerably under-utilized because access to original raw data is typically maintained by the source lab. On the other hand, there are many advantages in sharing complex image data in neuroscience research, including the opportunity for separate analysis of raw data by other scientists from another perspective and improved reproducibility of scientific studies and their results. Unfortunately, none of the neuroscience data sharing options that exist today fulfill all the needs of neuroscientists. To solve this problem, NeuroManager will include the following distinct, significant innovations: (i) versatility for handling two-dimensional (2D) and three-dimensional neuroimaging data sets from animal models and humans; (ii) functionality to share complex datasets that extends secure, privacy-controlled paradigms from institutional, laboratory-based and even public domains; (iii) flexibility to implement NeuroManager within an institute’s IT infrastructure, or on most cloud-based virtualized environments including Azure, Google Cloud Services and Amazon Web Services; (iv) and most importantly, the ICARUS technology for CBIR in neuroimaging data sets. The benefit of NeuroManager for the neuroscience research community, pharmacological and biotechnological R&D, and society in general will be to foster collaboration between scientists and institutions, promoting innovation through combined expertise in an interdisciplinary atmosphere. This will open new horizons for better understanding the neuropathology associated with several human neuropsychiatric and neurological conditions at various levels (i.e., macroscopically, microscopically, subcellularly and functionally), ultimately leading to an improved basis for developing novel treatment and prevention strategies for complex brain diseases. In Phase I we will prove feasibility of this novel technology by developing prototype software that will perform CBIR on 2D whole slide images of coronal sections of entire mouse brains from ongoing research projects of our collaborators. Work in Phase II will focus on developing the commercial software product that will include all of the innovations mentioned above. A competing technology with comparable functionality, addressing the full breadth of needs for modern neuroscience research, is currently not available commercially or otherwise.

该项目旨在开发神经管理器™，这是一个用于高级解析的创新神经信息学平台， 存储、聚合、分析和共享复杂的神经科学图像数据。我们将采用的核心技术 NeuroManager中的开发将是使用语义进行图像内容分析(ICARUS)， 智能神经图像管理系统，将支持基于视觉外观或通过 语义概念。Icarus将使用机器学习应用于基于内容的图像检索(CBIR)来构建 并优化总结微观和宏观图像外观的模型，并自动分配 从语义概念到神经图像。神经科学研究产生了广泛的、多方面的数据， 利用率很低，因为对原始原始数据的访问通常由源实验室维护。论 另一方面，在神经科学研究中共享复杂的图像数据有许多优势，包括 其他科学家从另一个角度单独分析原始数据的机会和改进 科学研究及其结果的重现性。不幸的是，没有一个神经科学数据共享选项 今天存在的可以满足神经科学家的所有需求。为了解决这个问题，NeuroManager将包括 以下是独特的重大创新：(I)处理二维(2D)和三维 来自动物模型和人类的神经成像数据集；(Ii)共享扩展的复杂数据集的功能 来自机构、实验室甚至公共领域的安全、隐私受控的范例；(3)灵活性 在研究所的IT基础设施中或在大多数基于云的虚拟化环境中实施NeuroManager 环境，包括Azure、Google Cloud Services和Amazon Web Services；(Iv)最重要的是， 用于神经成像数据集的CBIR的ICARUS技术。NeuroManager对神经科学的益处 研究界、药学和生物技术研发以及整个社会都将促进 科学家和机构之间的合作，通过在 跨学科的氛围。这将为更好地理解神经病理学开辟新的视野。 在不同水平上与几种人类神经精神和神经状况相关(即， 宏观上、微观上、亚细胞上和功能上)，最终导致 为复杂的脑部疾病开发新的治疗和预防策略。在第一阶段，我们将证明 通过开发对2D整个幻灯片执行CBIR的原型软件来验证这项新技术的可行性 来自我们的合作者正在进行的研究项目的整个小鼠大脑的冠状切片的图像。在.工作 第二阶段将专注于开发商业软件产品，其中将包括所有创新 如上所述。具有类似功能的竞争技术，可满足各种需求 对于现代神经科学研究，目前还不能商业化或以其他方式获得。