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
• 负责人: Thomas Macrina
• 金额: $ 16.27万
• 依托单位: ZETTA AI, LLC
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/10693397
• 关键词: Artificial Intelligence; Automation; BRAIN initiative; Basic Science; Brain; Brain Diseases; Caenorhabditis elegans; Client; Code; Communities; Courtship; Data; Data Set; Defect; Detection; Drosophila genus; Electron Microscope; Electron Microscopy; Encapsulated; Engineering; Goals; Human; Image; Image Analysis; Infrastructure; Ingestion; Internet; Manuals; Maps; Mining; Mission; Nerve; Nervous System; Pathology; Phase; Private Sector; Process; Recording of previous events; Reporting; Research; Resolution; Running; Schedule; Semantics; Speed; Synapses; System; Testing; Three-Dimensional Image; Untranslated RNA; Visual; analysis pipeline; cloud storage; computational pipelines; connectome; convolutional neural network; cost; data acquisition; data format; insight; medical schools; microscopic imaging; millimeter; neural circuit; operation; petabyte; prevent; reconstruction; success; transmission process; usability

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.

《大脑2025》报告指出，美国大脑倡议的一个主要目标是“生成电路图”，以及 电子显微镜(EM)被认为是“电路映射的黄金标准”。到目前为止，新兴市场是唯一的途径 它曾经提供了一个连接体，一张神经系统或大脑中所有突触连接的地图。之后 在20世纪80年代线虫连接体出现后，手工图像分析的劳动阻止了EM方法 推广到更大的神经系统。从那时起，劳动力因进步而大幅减少 人工智能。人类只需要纠正自动重建中仍然存在的错误。泽塔AI 成立的目的是使任何神经科学家都能进行连接图像分析。2021年，泽塔完成了 艾伦研究所的一个立方毫米皮质体积的自动重建。这是仅有的 世界上现存的三个PB级的重建。对于哈佛医学院，泽塔还完成了一项 果蝇腹侧神经索的自动重建。这些成功确立了泽塔作为领先的 连接学中的组织。泽塔的图像分析管道需要大量的工程劳动力才能运行。 根据我们过去两年的运作，我们已经确定了几个工程用工的机会 通过流程自动化进行减少，包括EM图像摄取、图像对齐和硬例挖掘。 这样的过程自动化将有助于让所有神经科学家都能接触到连接。神经网络的可用性 电路图将有助于发现连接病变和其他结构病理 推测与脑部疾病有关。