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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

基本信息

批准号：
10609151
负责人：
Paul Angstman
金额：
$ 5.5万
依托单位：
MICROBRIGHTFIELD, LLC
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-01 至 2022-08-18
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10609151
关键词：
3-Dimensional Acute Address Animal Model Area Artificial Intelligence Autopsy Axon Biotechnology Brain Diseases Classification Collaborations Communities Complex Computer Assisted Computer software Detection Development Feasibility Studies Fiber Goals Gold High Performance Computing Human Image Individual Knowledge Laboratories Learning Logic Machine Learning Methods Morphology National Institute of Mental Health Nature Neuraxis Neurodegenerative Disorders Neurodevelopmental Disorder Neurosciences Research Pattern Pharmacology Phase Physiological Publishing Research Research Personnel Societies Synapses System Technology Time Tissues Transgenic Animals Validation Work automated segmentation axon injury base convolutional neural network density fighting image processing improved innovation insight microscopic imaging nervous system disorder neural network neuropsychiatric disorder new technology next generation novel operation prevent prototype reconstruction research and development terabyte treatment strategy usability

项目摘要

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.

摘要