课题基金 / 基金详情

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

项目摘要

项目成果

JACOB R GLASER的其他基金

相似基金

相关文献

中文摘要
翻译
摘要 该第二阶段项目描述了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、追踪、重建和定量分析(具有 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,即使是轴突纤维密度极高的区域。准确和 严格分析非转基因小鼠CNS组织的3D和2D显微镜图像中可见的所有轴突纤维, 和转基因动物模型以及人类死后中枢神经系统组织的研究,有望使研究人员能够 获得新的见解生理神经网络连接模式以及神经病理 与人类神经精神和神经系统疾病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,我们创造了 HyperAxon原型软件,利用实验室构建的原创技术 轴突纤维在麻省理工学院林肯实验室(MIT LL)(列克星敦,MA)创建,并将该技术扩展为 几个新的专门的深度神经网络此外,我们证实了我们的方法将是成功的 in research研究applications应用.第一阶段的所有具体目标都已完全完成,证明了 成功开发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CNS区域的3D和2D显微镜图像中可见的所有轴突纤维, 高轴突纤维密度,(ii)识别轴突分支点,(iii)分辨纤维束中通道轴突纤维 从轴突终末野中的轴突纤维,(iv)识别显示急性轴突损伤的轴突纤维,以及(v)精确地 量化CNS组织中轴突纤维的数量和密度的变化。为了广泛传播这个 作为一项重要的新技术,我们计划在第二阶段结束时将HyperAxon软件商业化, 在Amazon Web Services(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将在未来的 这是神经科学研究领域的一个重要领域,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、神经变性和神经障碍相关的回路。 最终,这将为开发针对各种疾病的新型治疗策略奠定更好的基础。 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;第二阶段的工作将集中在为商业发布创建HyperAxon的完整功能。期间 在第二阶段,我们将密切合作,对HyperAxon进行广泛的产品验证和可用性研究 MIT LL和我们的学术合作伙伴。没有竞争技术。
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
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