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Abstract This Lab to Marketplace project describes the development of HyperAxon™, highly innovative software for performing automated segmentation, tracing, reconstruction and quantitative analysis of all axonal fibers visible in three-dimensional (3D) microscopic 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 microscopic images of CNS tissue of non-transgenic and transgenic animal models as well as in human post mortem CNS tissue holds the promise of 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. HyperAxon will be based on the highly innovative artificial intelligence technology Learning-based Tracing of Dense Axonal Fibers (LTDAF) that was recently developed at MIT Lincoln Laboratory (MIT LL) (Lexington, MA). This project will build upon the original, lab-built LTDAF technology to create commercial software for wide- spread dissemination of this important new technology. Dissemination of this technology via a Lab to Marketplace commercial product is consistent with NIMH goals and will result in the technology having a significant impact on neuroscience research. The game-changing innovation in HyperAxon is the ability to automatically (i) segment, trace and reconstruct all axonal fibers visible in 3D microscopic images of CNS areas with high axonal fiber density, (ii) identify axonal branch points, (iii) resolve axonal fibers of passage from axonal fibers that make presumptive synapses in target regions, (iv) identify axonal fibers showing acute axonal injury and (v) precisely quantify alterations in number and density of axonal fibers in CNS tissue. Based on published pilot work performed at MIT LL, 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 will demonstrate 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. We will perform extensive feasibility studies, 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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Next generation axonal quantification and classification using AI
  • 批准号:
    10324805
  • 项目类别:
  • 资助金额:
    $44.98万
  • 财政年份:
    2021
  • 负责人:
    Paul Angstman
  • 依托单位:
Neuroinformatics platform using machine learning and content-based image retrieval for neuroscience image data
  • 批准号:
    9797689
  • 项目类别:
  • 资助金额:
    $74.86万
  • 财政年份:
    2018
  • 负责人:
    Paul Angstman
  • 依托单位:
Neuroinformatics platform using machine learning and content-based image retrieval for neuroscience image data
  • 批准号:
    10251140
  • 项目类别:
  • 资助金额:
    $74.72万
  • 财政年份:
    2018
  • 负责人:
    Paul Angstman
  • 依托单位:
Automated 3D quantitative analysis of dendritic spines imaged with light microscopy
  • 批准号:
    9255387
  • 项目类别:
  • 资助金额:
    $80.0万
  • 财政年份:
    2016
  • 负责人:
    Paul Angstman
  • 依托单位:
海外基金