Infrastructure automation for connectomic image analysis
连接组图像分析的基础设施自动化
基本信息
- 批准号:10547607
- 负责人:
- 金额:$ 33.46万
- 依托单位:
- 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:2022-09-01 至 2024-02-28
- 项目状态:已结题
- 来源:
- 关键词:Artificial IntelligenceAutomationBRAIN initiativeBasic ScienceBrainBrain DiseasesCaenorhabditis elegansClientCodeCommunitiesCourtshipDataData SetDefectDetectionDrosophila genusElectron MicroscopeElectron MicroscopyEncapsulatedEngineeringGoalsGoldHumanImageImage AnalysisInfrastructureIngestionInstitutesInternetManualsMapsMiningMissionNerveNervous system structurePathologyPhasePrivate SectorProcessRecording of previous eventsReportingResearchRunningScheduleSemanticsSpeedSynapsesSystemTestingThree-Dimensional ImageUntranslated RNAVisualanalysis pipelinebasecloud storagecomputational pipelinesconnectomeconvolutional neural networkcostdata acquisitiondata formatinsightmedical schoolsmicroscopic imagingmillimeterneural circuitoperationpetabytepreventreconstructionsuccesstransmission process
项目摘要
The BRAIN 2025 report states that a major goal of the US BRAIN Initiative is "Generate circuit diagrams," and
identifies electron microscopy (EM) as "the gold standard for circuit mapping." So far EM is the only approach
that has ever delivered a connectome, a map of all synaptic connections in a nervous system or brain. After
the C. elegans connectome in the 1980s, the labor of manual image analysis prevented the EM approach from
generalizing to larger nervous systems. Since then, labor has been dramatically reduced by progress in
artificial intelligence. Humans need only correct the errors that remain in an automated reconstruction. Zetta AI
was founded to make connectomic image analysis accessible to any neuroscientist. In 2021, Zetta completed
an automated reconstruction of a cubic millimeter cortical volume for the Allen Institute. This is one of only
three existing petascale reconstructions in the world. For the Harvard Medical School, Zetta also completed an
automated reconstruction of the Drosophila ventral nerve cord. These successes establish Zetta as a leading
organization in connectomics. Zetta’s image analysis pipeline requires significant engineering labor to operate.
Based on our operations over the past two years, we have identified several opportunities for engineering labor
reduction by process automation, including EM image ingestion, image alignment, and hard example mining.
Such process automation will help make connectomics accessible to all neuroscientists. Availability of neural
circuit diagrams will aid the discovery of connectopathies and other structural pathologies that have long been
hypothesized to be associated with brain disorders.
BRAIN 2025报告指出,美国BRAIN计划的一个主要目标是“生成电路图”,
将电子显微镜(EM)视为“电路映射的黄金标准”。“到目前为止,EM是唯一的方法,
它曾经传递过一个连接体,一个神经系统或大脑中所有突触连接的地图。后
梭在20世纪80年代,手工图像分析的劳动阻止了EM方法,
一般化到更大的神经系统。从那时起,劳动力已经大大减少了进步,
人工智能人类只需要纠正自动重建中存在的错误。泽塔AI
是为了让神经科学家都能使用连接组学图像分析而成立的。2021年,泽塔完成了
一个立方毫米皮质体积的自动重建的艾伦研究所。这是世界上
世界上现存的三个千万亿次的重建。在哈佛医学院,泽塔还完成了一项
果蝇腹神经索的自动重建。这些成功使Zetta成为领先的
连接组学中的组织Zetta的图像分析管道需要大量的工程劳动力来操作。
根据我们过去两年的运营情况,
通过过程自动化减少,包括EM图像摄取,图像对齐和硬示例挖掘。
这种过程自动化将有助于所有神经科学家都能使用连接组学。神经可用性
电路图将有助于发现连接病和其他结构性病变,
被认为与脑部疾病有关
项目成果
期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Thomas Macrina其他文献
Thomas Macrina的其他文献
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{{ truncateString('Thomas Macrina', 18)}}的其他基金
Infrastructure automation for connectomic image analysis
连接组图像分析的基础设施自动化
- 批准号:
10693397 - 财政年份:2022
- 资助金额:
$ 33.46万 - 项目类别:
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