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Next generation axonal quantification and classification using AI

Next generation axonal quantification and classification using AI
使用人工智能的下一代轴突量化和分类
批准号:
10698843
负责人:
JACOB R GLASER
金额:
$87.3万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-08-19 至 2026-06-30

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中文摘要
翻译
摘要 这个第二阶段的项目描述了HyperAxon™的商业开发,这是一种高度创新的软件,用于 对所有轴突纤维进行自动分割、跟踪、重建和定量分析(使用 并且没有急性轴突损伤的迹象)在二维和三维(2D和3D)显微镜图像中可见 中枢神经系统(CNS)区域,甚至轴突纤维密度极高的区域。准确且 严格分析非转基因动物中枢神经系统组织3D和2D显微图像中可见的所有轴突纤维 转基因动物模型以及人类死后中枢神经系统组织有望使研究人员能够 获得对生理神经网络连接模式以及神经病理的新见解 与人类神经精神和神经紊乱相关的连接性改变的基础。 然而,这不能用当代的计算机辅助追踪和重建方法来实现, 这是目前研究轴突纤维的黄金标准,因为这些方法主要解决 仅对有限数量的单个轴突纤维进行追踪和重建。在第一阶段,我们创建了 HyperAxon原型软件,利用实验室构建的原创技术基于学习的密集跟踪 麻省理工学院林肯实验室(麻省理工学院林肯实验室)(马萨诸塞州列克星敦)创造的轴突纤维,并通过 几个新的、专门的深度神经网络。此外,我们还验证了我们的方法将是成功的 在研究应用方面。第一阶段各项具体目标全面完成,论证了 成功开发了HyperAxon。HyperAxon中改变游戏规则的创新是能够自动 (I)分割、追踪和重建中枢神经系统区域3D和2D显微镜图像中可见的所有轴突纤维 轴突纤维密度高,(Ii)识别轴突分支点,(Iii)分解纤维束中通过的轴突纤维 从轴突终末区域的轴突纤维中,(Iv)识别显示急性轴突损伤的轴突纤维,(V)准确地 量化CNS组织中轴突纤维的数量和密度的变化。广泛传播这一点 重要的新技术我们计划在第二阶段结束时将HyperAxon软件商业化 在亚马逊网络服务(AWS)和传统软件上运行的基于云的软件即服务 在本地机构计算机上运行的应用程序。我们相信,HyperAxon将在 神经科学研究领域,将使中枢神经系统改变的研究取得实质性进展 与神经发育、神经精神、神经退行性和神经性疾病相关的回路。 最终,这将为开发针对广泛疾病的新的治疗策略提供更好的基础。 复杂的脑部疾病。在第一阶段,我们通过开发原型证明了这项新技术的可行性。 软件;第二阶段的工作将专注于为商业发布创造HyperAxon的全部功能。在.期间 第二阶段,我们将与HyperAxon密切合作,进行广泛的产品验证和可用性研究 与麻省理工学院和我们的学术合作伙伴。目前还没有与之竞争的技术。
英文摘要
Abstract This Phase II project describes the commercial development of HyperAxon™, highly innovative software for performing automated segmentation, tracing, reconstruction and quantitative analysis of all axonal fibers (with and without signs of acute axonal injury) visible in two- and three-dimensional (2D and 3D) microscopy images of central nervous system (CNS) areas, even those with extremely high axonal fiber density. Accurate and rigorous analysis of all axonal fibers visible in 3D and 2D microscopy images of CNS tissue of non-transgenic and transgenic animal models as well as in human post mortem CNS tissue promises to enable researchers to gain novel insights into physiological neural network connectivity patterns as well as into the neuropathological underpinnings of alterations in connectivity associated with human neuropsychiatric and neurological disorders. However, this cannot be achieved with contemporary, computer-assisted tracing and reconstruction methods, which currently are the gold standard for investigating axonal fibers, because these methods primarily address tracing and reconstruction of only a limited number of individual axonal fibers. During Phase I we created HyperAxon prototype software by leveraging the original, lab-built technology Learning-based Tracing of Dense Axonal Fibers created at MIT Lincoln Laboratory (MIT LL) (Lexington, MA) and extending this technology with several new, specialized deep neural networks. Furthermore, we validated that our approach will be successful in research applications. All specific aims of Phase I were fully completed, demonstrating feasibility of successfully developing HyperAxon. The game-changing innovation in HyperAxon is the ability to automatically (i) segment, trace and reconstruct all axonal fibers visible in 3D and 2D microscopy images of CNS areas with high axonal fiber density, (ii) identify axonal branch points, (iii) resolve axonal fibers of passage in fiber tracts from those in axonal terminal fields, (iv) identify axonal fibers showing acute axonal injury and (v) precisely quantify alterations in number and density of axonal fibers in CNS tissue. For widespread dissemination of this important new technology we plan to commercialize the HyperAxon software at the end of Phase II as both a cloud-based “software as a service” running on Amazon Web Services (AWS) and traditional software application running on local institutional computers. We are convinced that HyperAxon will be impactful in the field of neuroscience research and will enable substantial advancements in research on alterations in CNS circuitry associated with neurodevelopmental, neuropsychiatric, neurodegenerative and neurological disorders. Ultimately, this will result in an improved basis for developing novel treatment strategies for a wide spectrum of complex brain diseases. In Phase I we demonstrated feasibility of this novel technology by developing prototype software; work in Phase II will focus on creating the full functionality of HyperAxon for commercial release. During Phase II we will perform extensive product validation and usability studies of HyperAxon in close collaboration with MIT LL and our academic collaboration partners. A competing technology is not available.
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Microscope system for large scale optical imaging of neuronal activity using kilohertz frame rates
  • 批准号:
    10541683
  • 项目类别:
  • 资助金额:
    $99.89万
  • 财政年份:
    2022
  • 负责人:
    JACOB R GLASER
  • 依托单位:
System for Volumetric 2-photon Imaging of Neuroactivity Using Light Beads Microscopy
  • 批准号:
    10755027
  • 项目类别:
  • 资助金额:
    $99.98万
  • 财政年份:
    2022
  • 负责人:
    JACOB R GLASER
  • 依托单位:
System for Volumetric 2-photon Imaging of Neuroactivity Using Light Beads Microscopy
  • 批准号:
    10603310
  • 项目类别:
  • 资助金额:
    $45.0万
  • 财政年份:
    2022
  • 负责人:
    JACOB R GLASER
  • 依托单位:
Microscope system for large scale optical imaging of neuronal activity using kilohertz frame rates
  • 批准号:
    10384932
  • 项目类别:
  • 资助金额:
    $99.53万
  • 财政年份:
    2022
  • 负责人:
    JACOB R GLASER
  • 依托单位:
海外基金